Determinants of stillbirth among deliveries attended in bale zone hospitals, oromia regional state, southeast ethiopia: A case–control study

International Journal of Women's Health, Volume 13, Year 2021

Interpretation

Background: Stillbirth is one of the adverse outcomes of pregnancy, and it is among the major public health problems in developing countries including Ethiopia. Stillbirth has wide-reaching consequences for parents, care providers, community and society at large. Purpose: To assess the determinant of stillbirth among deliveries attended in Bale zone hospitals Southeast Ethiopia. Methods: An institution-based unmatched case–control study was conducted. Cases were deliveries whose birth outcome was stillbirth and controls were deliveries with live birth. A pretested and structured checklist was used to collect data from a sample of 402 (134 cases and 268 controls). Systematic random sampling was used to recruit samples from a list of charts in the delivery registration book. Data were entered into EpiData version 4.2 and exported to SPSS version 20 for analysis. Crude and adjusted odds ratio with 95%CI was calculated and P-value <0.05 was used to declare statistical significance. Results: A total of 402 charts of mothers (134 cases and 268 controls) were included in the analysis. Preceding birth interval <24 months (AOR: 2.991; 95%CI: 1.351–6.621), antenatal visit started at third trimester (AOR: 2.739; 95%CI: 1.048–7.158), referred from other health facility (AOR: 3.215; 95%CI: 1.430–7.229), labor length ≥24 h (AOR: 3.169; 95%CI: 1.241–8.091), presence of meconium stained amniotic fluid (AOR: 2.670; 95%CI: 1.082– 6.592) and giving birth to a baby <2500 g (AOR: 3.155; 95%CI: 1.235–8.07) were determinants of stillbirth. Conclusion: Preceding birth interval of <24 months, antenatal visit started at third trimester, referred from other health facility, presence of meconium stained amniotic fluid, labor length ≤24 h and giving birth to a baby <2500 g were found the determinants of stillbirth. Intrapartum care, early identification of labor complications and referral system are required. The study was conducted at the five public hospitals found in Bale zone. Bale zone is located in Oromia regional state in Southeast Ethiopia. Robe town, the capital of Bale zone is found 430 km away from Addis Ababa, the capital city of Ethiopia. Currently, the zone has 21 districts of which three are town administrations and of the rural districts nine are agrarian and nine are agro-pastoralist. The zone has a total population of 1,888,366 of which 951,736 are male, 936,630 are female and the expected deliveries in the study year accounts are 65,526. A total of five public hospitals are found in the zone; namely Goba Referral Hospital, Robe General Hospital, Ginnir General Hospital, Dalo Mena General Hospital and Madda Walabu Primary Hospital. There are also 87 functional health centers in the zone. Concerning comprehensive maternal service delivery, all the hospitals deliver comprehensive emergency, obstetric, and neonatal care service and 76 health centers deliver basic emergency, obstetric and neonatal care services. The study was conducted from July 2018 to June 2019 and an institution-based unmatched case–control study design was employed. All charts of mothers who delivered from July 2018 to June 2019 in the five hospitals of Bale zone were the source population. All charts of mothers delivered from July 2018 to June 2019, which were selected by systematic random sampling as cases and controls in the five hospitals of Bale zone were the study population. Stillbirths whose charts are available and have full history recorded delivery summery and/or procedure notes during the study period were included in the study as cases. Live births, whose charts are available and have full history, delivery summery and or procedure notes during the study period were included in the study as controls. Twelve charts which were found incomplete on major variables under study (no information about antenatal period, labor status and/or delivery summary) and missed charts were excluded. Sample size is calculated using EpiInfo 7.0 StatCalc program by taking assumptions of 95% confidence level, two controls for each case, 80% power and the prevalence of exposure among control 7.1% with odds ratio 2.8 (1.78, 4.46) of variable low birth weight, were taken from an unmatched a case–control study done at Hawasa University Hospital, Ethiopia.12 A total sample size of 366 (122 cases and 244 controls) were calculated and adding 10% contingency for incomplete check list filled by data collectors and the final sample size was 402 (134 cases and 268 controls). A variable low birth weight was selected because it was the exposure variable that gave the highest sample size for cases and controls among the other variables in a study conducted in Hawasa University. The allocation of samples to each hospital was determined based on proportion of number of cases using report of the period (July 2018 – June 2019). Accordingly from Goba Referral Hospital 37 cases and 74 controls, Robe General Hospital 33 cases and 66 controls, Ginnir General Hospital 35 cases and 70 controls, Dalo Mena General Hospital 20 cases and 40 controls and Madda Walabu Primary Hospital nine cases and 18 controls were taken. Systematic random sampling with interval K=3 was used to recruit charts of cases after listing medical record numbers of all stillbirths in the study period from the delivery registration book of each hospital. Controls were also selected using systematic random sampling from the list of live births prepared from the delivery registration book and using the list as a sampling frame. Then, the selected charts, ie 134 for cases and 268 for controls were identified from the card room. The dependent variable for the study was stillbirth. And the independent variables were sociodemographic factors (age, residence, marital status), maternal health and pregnancy related factors (gravidity, parity, preceding birth interval, history of stillbirth, maternal medical illness, antenatal follow up, tetanus toxoid vaccination, iron folic acid supplementation, antepartum hemorrhage, hypertensive disorder of pregnancy, premature rupture of membrane), labor and delivery related factors (mode of admission, partograph use, fetal presentation, cord accident, obstructed labor, color of amniotic fluid, labor augmentation, duration of labor, mode of delivery) and fetal related factors (gestational age at birth, birth weight, number of newborns, congenital structure). Stillbirth: a baby born without any signs of life at or after 28 weeks of gestation or at least 1000 g in birth weight.1 Cases: were deliveries whose birth outcome was stillbirths, defined as babies born without any signs of life at or after 28 weeks of gestation or at least 1000 g in weight. Controls: were deliveries whose birth outcome of live births, defined as babies showing evidence of life (such as beating of the heart, pulsation of umbilical cord) on delivery at or after 28 weeks of gestation or at least 1000 g in weight. Partograph use: if the data on the three components of partograph (fetal condition, progress of labor, and maternal condition) were completed, it is considered that a partograph is utilized. Data was collected by using pretested and structured checklist which was developed in English language adapted from literature related to stillbirth and modified according to the local context by the investigators. The checklist consists of information on sociodemographic data, maternal health and pregnancy data, labor and delivery data and birth outcome data. Five data collectors who have midwifery profession were recruited for data collection and five senior midwives were also recruited for facilitation and supervision of daily data collection activities. During collection charts of cases and controls, the following steps were followed: first, all medical record numbers of stillbirths found in the study period were identified and listed from the delivery registration book of each hospital. Using a systematic random sampling method, cases were selected for each hospital proportionally to their annual stillbirth delivery load. Following selection of cases and controls, data collectors and card room workers have selected charts of mothers from card room using medial record numbers and reviewed the history, delivery summery, laboratory results, partograph, decision notes, progress notes, and operation notes and filled in the checklist. Incomplete charts on major variables under study (no information about antenatal period, labor status and/or delivery summary) were excluded. The selected charts were given study identification numbers to be used on the checklist for anonymity. Prior to data collection, the data collectors and supervisors were trained with a practical session for one day on techniques of sampling and data collection. Pretest was carried out on 5% of the samples before the actual data collection at Dodola General Hospital and modifications of the checklist were made on rephrasing and skipping patterns. In addition the average time required to complete one checklist was also estimated. Review of medical records was done carefully; charts with incomplete information on major variables under study such as information about antenatal period, labor status and/or delivery summary were excluded. Daily evaluation for completeness at the time of data collection was followed by the supervisor to assure collection of full information and appropriate documentation. The investigator has reviewed all checklists for omissions, clarity, and consistency of data to verify the completeness of the collected data. Data entering was performed using EpiData version 4.2 and coding, clearing and analysis was done using SPSS version 20 software statistical packages by the principal investigator. Frequency and proportions were used to describe the study population in relation to relevant variables. Those variables with a P<0.25 in the bivariate logistic regression analysis were entered to multivariate logistic regression analysis and declared significant at 95% confidence interval. Multiple logistic regression analysis was employed and significance was declared at P<0.05 and 95% confidence interval. Multicollinearity test was carried out to see the correlations between predictors of outcome variables. Finally the results were presented using tables and texts. Ethical clearance was secured and the official letter of permission was obtained from the Madda Walabu University, Goba Referral Hospital. Subsequently, a letter of permission was obtained from each hospital administrator and sent to each hospital maternal and child health units and medical record departments. The objective of the study was explained to the head of each unit and administrators of the hospitals. The permission of the patient to review their medical history was not required by the University of Madda Walabu and the hospitals. Personal identifiers such as name, phone number, etc have not been considered as a code and a care number has been used. At the end of the data collection process, strict attention was paid to the selected charts of study participants during the data collection phase, until the return was respected at the end of the data collection. All information taken from the client charts was kept confidential and only investigators had access to the information which was used only for the purpose of this study.

AI Digest

Study Justification:

– Stillbirth is a significant public health issue in developing countries, including Ethiopia.
– Understanding the determinants of stillbirth is crucial for developing effective interventions and reducing stillbirth rates.
– This study aimed to assess the determinants of stillbirth among deliveries attended in Bale zone hospitals in Southeast Ethiopia.

Highlights:

– The study included 402 charts of mothers (134 cases and 268 controls) from five public hospitals in Bale zone.
– Determinants of stillbirth identified in the study included preceding birth interval

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a clear description of the study design, sample size calculation, data collection methods, and statistical analysis. However, it lacks information on the representativeness of the study population and the generalizability of the findings. To improve the evidence, the authors could include details on the selection criteria for the hospitals and participants, as well as the demographic characteristics of the study population. Additionally, they could discuss the limitations of the study and suggest areas for further research.

Abstract

The study was conducted at the five public hospitals found in Bale zone. Bale zone is located in Oromia regional state in Southeast Ethiopia. Robe town, the capital of Bale zone is found 430 km away from Addis Ababa, the capital city of Ethiopia. Currently, the zone has 21 districts of which three are town administrations and of the rural districts nine are agrarian and nine are agro-pastoralist. The zone has a total population of 1,888,366 of which 951,736 are male, 936,630 are female and the expected deliveries in the study year accounts are 65,526. A total of five public hospitals are found in the zone; namely Goba Referral Hospital, Robe General Hospital, Ginnir General Hospital, Dalo Mena General Hospital and Madda Walabu Primary Hospital. There are also 87 functional health centers in the zone. Concerning comprehensive maternal service delivery, all the hospitals deliver comprehensive emergency, obstetric, and neonatal care service and 76 health centers deliver basic emergency, obstetric and neonatal care services. The study was conducted from July 2018 to June 2019 and an institution-based unmatched case–control study design was employed. All charts of mothers who delivered from July 2018 to June 2019 in the five hospitals of Bale zone were the source population. All charts of mothers delivered from July 2018 to June 2019, which were selected by systematic random sampling as cases and controls in the five hospitals of Bale zone were the study population. Stillbirths whose charts are available and have full history recorded delivery summery and/or procedure notes during the study period were included in the study as cases. Live births, whose charts are available and have full history, delivery summery and or procedure notes during the study period were included in the study as controls. Twelve charts which were found incomplete on major variables under study (no information about antenatal period, labor status and/or delivery summary) and missed charts were excluded. Sample size is calculated using EpiInfo 7.0 StatCalc program by taking assumptions of 95% confidence level, two controls for each case, 80% power and the prevalence of exposure among control 7.1% with odds ratio 2.8 (1.78, 4.46) of variable low birth weight, were taken from an unmatched a case–control study done at Hawasa University Hospital, Ethiopia.12 A total sample size of 366 (122 cases and 244 controls) were calculated and adding 10% contingency for incomplete check list filled by data collectors and the final sample size was 402 (134 cases and 268 controls). A variable low birth weight was selected because it was the exposure variable that gave the highest sample size for cases and controls among the other variables in a study conducted in Hawasa University. The allocation of samples to each hospital was determined based on proportion of number of cases using report of the period (July 2018 – June 2019). Accordingly from Goba Referral Hospital 37 cases and 74 controls, Robe General Hospital 33 cases and 66 controls, Ginnir General Hospital 35 cases and 70 controls, Dalo Mena General Hospital 20 cases and 40 controls and Madda Walabu Primary Hospital nine cases and 18 controls were taken. Systematic random sampling with interval K=3 was used to recruit charts of cases after listing medical record numbers of all stillbirths in the study period from the delivery registration book of each hospital. Controls were also selected using systematic random sampling from the list of live births prepared from the delivery registration book and using the list as a sampling frame. Then, the selected charts, ie 134 for cases and 268 for controls were identified from the card room. The dependent variable for the study was stillbirth. And the independent variables were sociodemographic factors (age, residence, marital status), maternal health and pregnancy related factors (gravidity, parity, preceding birth interval, history of stillbirth, maternal medical illness, antenatal follow up, tetanus toxoid vaccination, iron folic acid supplementation, antepartum hemorrhage, hypertensive disorder of pregnancy, premature rupture of membrane), labor and delivery related factors (mode of admission, partograph use, fetal presentation, cord accident, obstructed labor, color of amniotic fluid, labor augmentation, duration of labor, mode of delivery) and fetal related factors (gestational age at birth, birth weight, number of newborns, congenital structure). Stillbirth: a baby born without any signs of life at or after 28 weeks of gestation or at least 1000 g in birth weight.1 Cases: were deliveries whose birth outcome was stillbirths, defined as babies born without any signs of life at or after 28 weeks of gestation or at least 1000 g in weight. Controls: were deliveries whose birth outcome of live births, defined as babies showing evidence of life (such as beating of the heart, pulsation of umbilical cord) on delivery at or after 28 weeks of gestation or at least 1000 g in weight. Partograph use: if the data on the three components of partograph (fetal condition, progress of labor, and maternal condition) were completed, it is considered that a partograph is utilized. Data was collected by using pretested and structured checklist which was developed in English language adapted from literature related to stillbirth and modified according to the local context by the investigators. The checklist consists of information on sociodemographic data, maternal health and pregnancy data, labor and delivery data and birth outcome data. Five data collectors who have midwifery profession were recruited for data collection and five senior midwives were also recruited for facilitation and supervision of daily data collection activities. During collection charts of cases and controls, the following steps were followed: first, all medical record numbers of stillbirths found in the study period were identified and listed from the delivery registration book of each hospital. Using a systematic random sampling method, cases were selected for each hospital proportionally to their annual stillbirth delivery load. Following selection of cases and controls, data collectors and card room workers have selected charts of mothers from card room using medial record numbers and reviewed the history, delivery summery, laboratory results, partograph, decision notes, progress notes, and operation notes and filled in the checklist. Incomplete charts on major variables under study (no information about antenatal period, labor status and/or delivery summary) were excluded. The selected charts were given study identification numbers to be used on the checklist for anonymity. Prior to data collection, the data collectors and supervisors were trained with a practical session for one day on techniques of sampling and data collection. Pretest was carried out on 5% of the samples before the actual data collection at Dodola General Hospital and modifications of the checklist were made on rephrasing and skipping patterns. In addition the average time required to complete one checklist was also estimated. Review of medical records was done carefully; charts with incomplete information on major variables under study such as information about antenatal period, labor status and/or delivery summary were excluded. Daily evaluation for completeness at the time of data collection was followed by the supervisor to assure collection of full information and appropriate documentation. The investigator has reviewed all checklists for omissions, clarity, and consistency of data to verify the completeness of the collected data. Data entering was performed using EpiData version 4.2 and coding, clearing and analysis was done using SPSS version 20 software statistical packages by the principal investigator. Frequency and proportions were used to describe the study population in relation to relevant variables. Those variables with a P<0.25 in the bivariate logistic regression analysis were entered to multivariate logistic regression analysis and declared significant at 95% confidence interval. Multiple logistic regression analysis was employed and significance was declared at P<0.05 and 95% confidence interval. Multicollinearity test was carried out to see the correlations between predictors of outcome variables. Finally the results were presented using tables and texts. Ethical clearance was secured and the official letter of permission was obtained from the Madda Walabu University, Goba Referral Hospital. Subsequently, a letter of permission was obtained from each hospital administrator and sent to each hospital maternal and child health units and medical record departments. The objective of the study was explained to the head of each unit and administrators of the hospitals. The permission of the patient to review their medical history was not required by the University of Madda Walabu and the hospitals. Personal identifiers such as name, phone number, etc have not been considered as a code and a care number has been used. At the end of the data collection process, strict attention was paid to the selected charts of study participants during the data collection phase, until the return was respected at the end of the data collection. All information taken from the client charts was kept confidential and only investigators had access to the information which was used only for the purpose of this study.

Materials

N/A

Innovations

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

1. Telemedicine: Implementing telemedicine services can help improve access to maternal health by allowing pregnant women in remote areas to consult with healthcare professionals through video calls or phone calls. This can provide them with necessary prenatal care and guidance without the need for physical travel.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower pregnant women to take control of their own health. These apps can provide educational materials, appointment reminders, and access to healthcare professionals for consultations.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services and education in rural areas can help bridge the gap in access to healthcare. These workers can conduct antenatal visits, provide health education, and refer women to higher-level healthcare facilities when necessary.

4. Transport and referral systems: Establishing efficient transport and referral systems can ensure that pregnant women in remote areas have access to timely emergency obstetric care. This can involve setting up emergency transportation services and strengthening communication channels between healthcare facilities.

5. Maternal waiting homes: Building maternal waiting homes near healthcare facilities can provide a safe and comfortable place for pregnant women to stay during the final weeks of pregnancy. This can ensure that they are close to the healthcare facility when labor begins, reducing the risk of delays in accessing care.

6. Task-shifting and training: Training and empowering non-specialist healthcare providers, such as midwives and nurses, to provide comprehensive maternal health services can help alleviate the shortage of skilled healthcare professionals in remote areas. This can include training them in emergency obstetric care and basic surgical skills.

7. Health education and awareness campaigns: Conducting targeted health education and awareness campaigns can help improve knowledge and awareness about maternal health issues in communities. This can include educating women and their families about the importance of antenatal care, birth preparedness, and the signs of complications during pregnancy.

These innovations can help address the challenges faced in improving access to maternal health in remote areas, ultimately reducing stillbirth rates and improving maternal and newborn outcomes.

AI Innovations Description

Based on the information provided, the study conducted in Bale zone hospitals in Southeast Ethiopia identified several determinants of stillbirth. These determinants include:

1. Preceding birth interval of less than 24 months.
2. Antenatal visit started at the third trimester.
3. Referred from another health facility.
4. Labor length of 24 hours or more.
5. Presence of meconium stained amniotic fluid.
6. Giving birth to a baby weighing less than 2500 grams.

To improve access to maternal health and reduce stillbirth rates, the following recommendations can be considered:

1. Strengthen family planning services: Promote and provide access to family planning methods to ensure adequate spacing between pregnancies, as a preceding birth interval of less than 24 months was identified as a determinant of stillbirth.

2. Enhance antenatal care services: Encourage early initiation of antenatal visits, ideally during the first trimester, to ensure comprehensive care throughout pregnancy. This can help identify and manage any potential risk factors for stillbirth.

3. Improve referral systems: Enhance the coordination and communication between health facilities to ensure timely and appropriate referrals for high-risk pregnancies. This can help ensure that pregnant women receive the necessary specialized care when needed.

4. Strengthen intrapartum care: Improve the quality of care during labor and delivery, including monitoring and management of labor complications. This can help reduce the risk of stillbirth associated with prolonged labor and meconium stained amniotic fluid.

5. Enhance newborn care: Provide adequate support and care for low birth weight babies, including appropriate feeding, temperature regulation, and monitoring for any complications. This can help improve the survival rate of newborns at risk of stillbirth.

6. Promote community awareness: Conduct community-based education and awareness programs to increase knowledge about the importance of maternal health, including the prevention of stillbirth. This can help empower communities to take proactive measures and seek timely care during pregnancy.

By implementing these recommendations, it is possible to improve access to maternal health services and reduce the incidence of stillbirth in the Bale zone and similar settings.

AI Innovations Methodology

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

1. Strengthen antenatal care services: Increase awareness and utilization of antenatal care by promoting early initiation of visits and ensuring regular follow-ups throughout pregnancy. This can be achieved through community outreach programs, education campaigns, and improving the availability and accessibility of antenatal care facilities.

2. Enhance referral systems: Improve the coordination and effectiveness of referral systems between health facilities to ensure timely access to emergency obstetric care. This can involve training healthcare providers on the appropriate referral protocols, establishing communication channels, and strengthening transportation services for pregnant women in need of specialized care.

3. Promote birth spacing: Educate women and families about the importance of birth spacing and provide access to family planning services. Encouraging a minimum birth interval of 24 months between pregnancies can help reduce the risk of stillbirth and other adverse pregnancy outcomes.

4. Improve intrapartum care: Enhance the quality of care during labor and delivery by ensuring skilled attendance, monitoring fetal well-being, and promptly addressing complications. This can be achieved through training healthcare providers, implementing evidence-based protocols, and ensuring the availability of essential equipment and supplies.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the number of antenatal care visits, percentage of timely referrals, birth spacing intervals, and quality of intrapartum care.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, interviews, or analysis of existing health records.

3. Introduce the recommendations: Implement the recommended interventions in a selected area or population. This could involve training healthcare providers, establishing referral networks, conducting awareness campaigns, and improving infrastructure and resources.

4. Monitor and evaluate: Continuously monitor the implementation of the interventions and collect data on the selected indicators. This can be done through regular data collection, surveys, or interviews with healthcare providers and beneficiaries.

5. Analyze the data: Compare the post-intervention data with the baseline data to assess the impact of the recommendations on the selected indicators. Use statistical analysis to determine if there are significant improvements in access to maternal health.

6. Adjust and refine: Based on the findings, make any necessary adjustments or refinements to the interventions. This could involve scaling up successful interventions, addressing identified challenges, and adapting strategies to the specific context.

7. Repeat the process: Continuously repeat the monitoring and evaluation process to track progress and make further improvements over time.

By following this methodology, it is possible to simulate the impact of the recommendations on improving access to maternal health and identify effective strategies for implementation.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.2147/IJWH.S276638

Research Areas:

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

Study Countries:

Ethiopia

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female