Impact of a maternal and newborn health results-based financing intervention (RBF4MNH) on stillbirth: a cross-sectional comparison in four districts in Malawi

BMC Pregnancy and Childbirth, Volume 21, No. 1, Year 2021

Interpretation

Background: Malawi implemented a Results Based Financing (RBF) model for Maternal and Newborn Health, “RBF4MNH” at public hospitals in four Districts, with the aim of improving health outcomes. We used this context to seek evidence for the impact of this intervention on rates of antepartum and intrapartum stillbirth, taking women’s risk factors into account. Methods: We used maternity unit delivery registers at hospitals in four districts of Malawi to obtain information about stillbirths. We purposively selected two districts hosting the RBF4MNH intervention and two non-intervention districts for comparison. Data were extracted from the maternity registers and used to develop logistic regression models for variables associated with fresh and macerated stillbirth. Results: We identified 67 stillbirths among 2772 deliveries representing 24.1 per 1000 live births of which 52% (n = 35) were fresh (intrapartum) stillbirths and 48% (n = 32) were macerated (antepartum) losses. Adjusted odds ratios (aOR) for fresh and macerated stillbirth at RBF versus non-RBF sites were 2.67 (95%CI 1.24 to 5.57, P = 0.01) and 7.27 (95%CI 2.74 to 19.25 P < 0.001) respectively. Among the risk factors examined, gestational age at delivery was significantly associated with increased odds of stillbirth. Conclusion: The study did not identify a positive impact of this RBF model on the risk of fresh or macerated stillbirth. Within the scientific limitations of this non-randomised study using routinely collected health service data, the findings point to a need for rigorously designed and tested interventions to strengthen service delivery with a focus on the elements needed to ensure quality of intrapartum care, in order to reduce the burden of stillbirths. This was a quantitative cross-sectional study which used routinely collected hospital data in the referral hospitals serving the districts of Ntcheu, Dedza, Salima (Central region) and Thyolo (Southern region). These district hospitals provide a secondary level of care and serve as the referral hospitals for all the primary health centres in their respective districts. The RBF districts were purposively selected because of ready access to hospital records. The non-RBF comparison districts were randomly selected by applying a random number table to a list of Malawi districts. The primary outcome for analysis was stillbirth, defined as an infant born with no signs of life at or after 28 weeks gestation [13]. A power calculation to determine the sufficiency of the number of register records was performed using Open Epi version 3 resulting in a sample of 2800 with a power of 90%, at a statistical significance level of 5%. This was based on anticipated stillbirth rates of 15 per 1000 live births in the combined population of the intervention hospitals and 34 per 1000 in the combined population in the non-intervention hospitals. These assumptions were based on initial scrutiny of District Health Management Information System (DHIS-2) returns. Data were extracted from the registers which are used in maternity units to prepare routine monthly reports. Data collected included maternal age, gravidity (number of pregnancies), and gestational age in weeks, preeclampsia, low birth weight and stillbirths. We used assessment of gestational age and birth weight recorded in the registers following the routine practice of health facilities. In Malawi, most women do not undergo sonography to confirm gestational age. In these hospitals, weighing of the newborns is done by the midwives using routinely available hospital weighing scales. A prepared data extraction tool in the form of a register was used. Data were entered into Microsoft Excel and checked for accuracy, consistency, and completeness. Analysis was undertaken using STATA version 14. For continuous variables, means and standard deviations were considered and presented. Logistic regression models were developed to determine the odds ratio for stillbirth under intervention and non-intervention conditions, and to assess whether the statistical relationship was confounded by other factors. The possible confounders included in the multivariable models were; low birth weight, gestational age, gravidity and a diagnosis of pre-eclampsia, based on findings in two previous local studies [6, 7]. The study was approved on a waiver of the need for individual participant consent by the nationally mandated College of Medicine Research and Ethics Committee (COMREC) and administrative permission for access was granted by the Hospital authorities.

AI Digest

Study Justification:

The study aimed to evaluate the impact of a Results Based Financing (RBF) model for Maternal and Newborn Health (RBF4MNH) on stillbirth rates in four districts in Malawi. The RBF4MNH intervention was implemented in public hospitals with the goal of improving health outcomes. The study sought to provide evidence on whether this intervention had a positive effect on reducing stillbirth rates, taking into account women’s risk factors.

Highlights:

– The study analyzed data from maternity unit delivery registers in four districts of Malawi.
– Two districts with the RBF4MNH intervention were compared to two non-intervention districts.
– A total of 67 stillbirths were identified among 2772 deliveries, representing a stillbirth rate of 24.1 per 1000 live births.
– The study found that the odds of fresh (intrapartum) stillbirth were 2.67 times higher in RBF sites compared to non-RBF sites.
– The odds of macerated (antepartum) stillbirth were 7.27 times higher in RBF sites compared to non-RBF sites.
– Gestational age at delivery was identified as a significant risk factor for stillbirth.

Recommendations:

– The study suggests a need for rigorously designed and tested interventions to strengthen service delivery and improve the quality of intrapartum care.
– The focus should be on reducing the burden of stillbirths by addressing the elements necessary for ensuring high-quality care during childbirth.

Key Role Players:

– Researchers and scientists specializing in maternal and newborn health.
– Health policymakers and government officials responsible for implementing interventions and improving healthcare services.
– Hospital authorities and administrators who can facilitate the implementation of recommended interventions.
– Healthcare providers, including midwives and obstetricians, who play a crucial role in delivering quality care during childbirth.
– Community health workers and volunteers who can support awareness campaigns and education on maternal and newborn health.

Cost Items for Planning Recommendations:

– Training programs for healthcare providers to enhance their skills and knowledge in intrapartum care.
– Procurement of necessary medical equipment and supplies for safe childbirth.
– Development and implementation of monitoring and evaluation systems to assess the impact of interventions.
– Community engagement activities, including awareness campaigns and education programs.
– Research and evaluation activities to continuously assess the effectiveness of interventions and identify areas for improvement.

Strength of Evidence

N/A

Abstract

This was a quantitative cross-sectional study which used routinely collected hospital data in the referral hospitals serving the districts of Ntcheu, Dedza, Salima (Central region) and Thyolo (Southern region). These district hospitals provide a secondary level of care and serve as the referral hospitals for all the primary health centres in their respective districts. The RBF districts were purposively selected because of ready access to hospital records. The non-RBF comparison districts were randomly selected by applying a random number table to a list of Malawi districts. The primary outcome for analysis was stillbirth, defined as an infant born with no signs of life at or after 28 weeks gestation [13]. A power calculation to determine the sufficiency of the number of register records was performed using Open Epi version 3 resulting in a sample of 2800 with a power of 90%, at a statistical significance level of 5%. This was based on anticipated stillbirth rates of 15 per 1000 live births in the combined population of the intervention hospitals and 34 per 1000 in the combined population in the non-intervention hospitals. These assumptions were based on initial scrutiny of District Health Management Information System (DHIS-2) returns. Data were extracted from the registers which are used in maternity units to prepare routine monthly reports. Data collected included maternal age, gravidity (number of pregnancies), and gestational age in weeks, preeclampsia, low birth weight and stillbirths. We used assessment of gestational age and birth weight recorded in the registers following the routine practice of health facilities. In Malawi, most women do not undergo sonography to confirm gestational age. In these hospitals, weighing of the newborns is done by the midwives using routinely available hospital weighing scales. A prepared data extraction tool in the form of a register was used. Data were entered into Microsoft Excel and checked for accuracy, consistency, and completeness. Analysis was undertaken using STATA version 14. For continuous variables, means and standard deviations were considered and presented. Logistic regression models were developed to determine the odds ratio for stillbirth under intervention and non-intervention conditions, and to assess whether the statistical relationship was confounded by other factors. The possible confounders included in the multivariable models were; low birth weight, gestational age, gravidity and a diagnosis of pre-eclampsia, based on findings in two previous local studies [6, 7]. The study was approved on a waiver of the need for individual participant consent by the nationally mandated College of Medicine Research and Ethics Committee (COMREC) and administrative permission for access was granted by the Hospital authorities.

Materials

N/A

Innovations

Based on the provided description, it seems that the study focused on evaluating the impact of a Results Based Financing (RBF) model on stillbirth rates in maternal and newborn health in Malawi. The study used maternity unit delivery registers from four districts in Malawi to collect data on stillbirths and other variables. Logistic regression models were developed to analyze the data and determine the odds ratio for stillbirth under intervention and non-intervention conditions.

In terms of potential innovations to improve access to maternal health, here are a few recommendations:

1. Implementing RBF models: Despite the findings of this particular study, RBF models have shown promise in improving health outcomes in other contexts. Further research and evaluation could be conducted to identify the specific elements needed to ensure the effectiveness of RBF models in reducing stillbirth rates and improving overall maternal health.

2. Strengthening service delivery: The study highlights the need for rigorously designed and tested interventions to strengthen service delivery, particularly in intrapartum care. Innovations could focus on improving the quality of care provided during labor and delivery, such as training healthcare providers, ensuring availability of necessary equipment and supplies, and implementing evidence-based protocols and guidelines.

3. Enhancing data collection and analysis: The study utilized routinely collected hospital data, but there may be opportunities to enhance data collection and analysis methods. Innovations could include the use of electronic health records, mobile health technologies, and data analytics tools to improve the accuracy, consistency, and completeness of data, as well as facilitate real-time monitoring and evaluation of maternal health indicators.

4. Addressing risk factors: The study identified gestational age at delivery as a significant risk factor for stillbirth. Innovations could focus on interventions to address risk factors associated with stillbirth, such as improving access to antenatal care, promoting healthy behaviors during pregnancy, and providing appropriate interventions for high-risk pregnancies.

5. Community engagement and education: Innovations could involve community engagement and education programs to raise awareness about maternal health, promote early and regular prenatal care, and encourage women to seek timely and appropriate healthcare services. This could include the use of community health workers, mobile health messaging, and community-based support groups.

It is important to note that these recommendations are based on the information provided and should be further explored and evaluated in the context of specific settings and populations.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the study is to design and implement rigorously tested interventions that focus on strengthening service delivery and ensuring quality intrapartum care. This is important in order to reduce the burden of stillbirths.

Specifically, the innovation should address the following elements:

1. Strengthening service delivery: The intervention should aim to improve the overall quality of maternal health services, including antenatal care, delivery, and postnatal care. This can be achieved through training healthcare providers, ensuring availability of essential equipment and supplies, and improving the infrastructure of healthcare facilities.

2. Focus on intrapartum care: The intervention should prioritize improving the quality of care during labor and delivery. This can include ensuring skilled attendance at birth, promoting evidence-based practices such as active management of labor, and providing timely interventions for complications.

3. Addressing risk factors: The intervention should target known risk factors for stillbirth, such as gestational age at delivery. This can be done through early identification and management of high-risk pregnancies, including appropriate monitoring and timely interventions.

4. Monitoring and evaluation: The innovation should include a robust monitoring and evaluation component to assess the impact of the intervention on maternal and newborn health outcomes. This can involve collecting and analyzing data on stillbirth rates, maternal mortality, and other relevant indicators.

It is important to note that the innovation should be rigorously designed and tested to ensure its effectiveness. This can involve conducting randomized controlled trials or other rigorous study designs to evaluate the impact of the intervention. Additionally, collaboration with relevant stakeholders, including healthcare providers, policymakers, and communities, is crucial for the successful implementation of the innovation.

AI Innovations Methodology

Based on the provided description, the study conducted a quantitative cross-sectional analysis to assess the impact of a Results Based Financing (RBF) intervention on stillbirth rates in four districts in Malawi. The methodology involved the use of maternity unit delivery registers to collect data on stillbirths, as well as other variables such as maternal age, gravidity, gestational age, preeclampsia, and low birth weight. Logistic regression models were developed to determine the odds ratio for stillbirth under intervention and non-intervention conditions, while controlling for potential confounding factors such as low birth weight, gestational age, gravidity, and pre-eclampsia. The study used a sample size of 2800, with a power of 90% and a statistical significance level of 5%. Data were extracted from the registers, entered into Microsoft Excel, and analyzed using STATA version 14. The study was approved by the College of Medicine Research and Ethics Committee (COMREC) and administrative permission for access to the hospital records was obtained.

Author

Dima Health

Statistics:

Citations: 1

Identifiers:

DOI: 10.1186/s12884-021-03867-6

Research Areas:

Community Interventions, Health System and Policy, Maternal and Child Health, Quality of Care, Workforce

Study Countries:

Malawi

Study Design:

Case-Control Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Mixed-methods, Quantitative, Systematic Review

Participants Gender:

Female