Early detection of maternal deaths in Senegal through household-based death notification integrating verbal and social autopsy: A community-level case study

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Study Justification:
– Reliable detection of maternal deaths is crucial for understanding barriers to care and developing targeted interventions.
– The study aimed to identify factors contributing to maternal deaths in Senegal and improve the quality of care.
Highlights:
– The study used household-level surveillance to detect maternal deaths in real-time and uncover clinical and social factors contributing to mortality.
– Maternal mortality rates in the study area increased from 67/100,000 births in 2009 to 392/100,000 births in 2011.
– Verbal autopsy analyses revealed a high proportion of maternal deaths occurring at the referral hospital, mainly due to inadequate case management of post-partum hemorrhage.
– Inefficiencies in the care continuum were identified, with potential negative impacts beyond the study community.
Recommendations:
– Improve case management of post-partum hemorrhage at the referral hospital.
– Address inefficiencies in the care continuum to ensure quality care for all women.
– Strengthen the referral network and ensure timely access to appropriate care.
– Implement rapid-cycle quality improvement interventions based on real-time community-level mortality trends.
Key Role Players:
– Community health workers (CHWs)
– Local and regional government health staff
– Hospital staff
– West Africa Regional MVP staff
– New York-based MVP staff
Cost Items for Planning Recommendations:
– Strengthening health facilities and infrastructure
– Scaling up human resources
– Training and retraining of CHWs
– Development and implementation of mHealth data collection tools
– Monitoring and evaluation of interventions
– Quality improvement efforts and meetings

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it presents a case study with specific data and findings. However, the sample size of maternal deaths is small, limiting the statistical analysis. To improve the evidence, a larger sample size could be collected over a longer period of time to strengthen the statistical analysis and provide more robust conclusions.

Background: Reliable detection of maternal deaths is an essential prerequisite for successful diagnosis of barriers to care and formulation of relevant targeted interventions. In a community-level case study, the use of household-level surveillance in Senegal unveiled an apparent increase in maternal deaths, which triggered a rapid-cycle collaborative response to implement a multipronged set of quick-win and sustained interventions intended to improve quality care. Methods: Part of a multi-country effort, the Millennium Villages Project is implementing a routine community-level information system in Senegal, able to detect maternal deaths in real-time and uncover clinical and social factors contributing to mortality. Within this geographically demarcated area of approximately 32 000 inhabitants, with a well-structured health system with patient referral services, deaths were registered and notified by community health workers, followed by timely verbal and social autopsies. Using the Pathway to Survival conceptual framework, case analysis and mortality reviews were conducted for evaluation and quality improvement purposes. Results: The estimated maternal mortality rates rose from 67/100000 births in 2009 (1 death), to 202/100000 births in 2010 (3 deaths) and 392/100000 births (5 deaths) in 2011. Although absolute numbers of maternal deaths remained too small for robust statistical analysis, following verbal autopsy analyses in 2011, it became evident that an unexpectedly high proportion of maternal deaths were occurring at the referral hospital, mostly post-Caesarian section. Inadequate case management of post-partum haemorrhage at the referral hospital was the most frequently identified probable cause of death. A joint task team systematically identified several layers of inefficiencies, with a potential negative impact on a larger catchment area than the study community. Conclusions: In this study, routine community-based surveillance identified inefficiencies at a tertiary level of care. Community-level surveillance systems that include pregnancy, birth and death tracking through household visits by community health workers , combined with verbal and social autopsy can identify barriers within the continuum of maternal care. Use of mHealth data collection tools sensitive enough to detect small changes in community-level mortality trends in real-time, can facilitate rapid-cycle quality improvement interventions, particularly when associated with social accountability structures of mortality reviews.

The Senegal MVP case study, involving a case-series analysis and subsequent intervention for maternal deaths, was prompted by an apparent increase in maternal mortality trends and the findings of integrated VASA. The case-series included all recorded maternal deaths from 2011, and can be considered opportunistic in that it was nested within routine data collection and feedback activities conducted by CHWs as part of the much broader ten-year MVP study. Routine vital statistics tracking and VASA began prior to the study period and continued beyond. Maternal mortality trends are presented as numbers of recorded maternal deaths from the start of 2007 to the end of 2012. Cases of maternal death were identified via active household-level surveillance of pregnancies, births and deaths, using mHealth platform Childcare+. A standardized VASA questionnaire was used to collect descriptive case data retrospectively following the death of any women aged 12–49 who lived within the geographical study area. Maternal deaths were defined as deaths of women while pregnant or within 42 days post-delivery, regardless of cause of death. Cases were assessed individually with VASA, and then collectively as part of a case-series analysis. The Pathway to Survival framework was used to identify areas of failure within the care continuum, and a rapid cycle change model (Figure 1) was used as a conceptual framework to guide quality improvement discussions and interventions. Modified rapid cycle change model for quality improvement. The study was conducted in a geographically demarcated area in northwestern Senegal, at one of the core MVP study sites. The densely-populated study area consists of a cluster of coastal villages and is home to approximately 32,000 individuals. The entire community received the complete integrated package of MVP interventions. When MVP was initiated in the cluster in 2006, there were 16 health posts (case de santé) and one primary health care clinic (PHC) (poste de santé). Working with government health bodies at local and regional levels, four other PHC clinics were added by 2010, strategically located throughout the cluster, to strengthen the local health system infrastructure and allow geographic access. Van Lerberghe et al. argue in a recent Lancet series that expanding coverage of health facilities is the first step to strengthening health systems for maternal survival, which is followed by scaling up human resources [25]. PHC clinics were staffed by a minimum of one nurse (Infirmière) and one midwife (Sage-femme), and designed to provide a comprehensive package of services, including facility-based deliveries supported by a midwife. The smaller health posts were designed to offer more basic health services under the auspices of a nurse or lay CHW minimally-trained in obstetric care. An extensive referral network, intended to achieve a continuum of care, was set-up. This involved basic ambulances to link households and health posts to PHCs, and an equipped ambulance service to link PHCs to the nearest hospital, located approximately seven kilometers away from the MVP site, outside of the study area boundary. All households were covered by routine CHW program activities including pregnancy, birth and death tracking at the household level. The main caregivers of women who died within childbearing age were approached and invited to participate in VASA. Once consent was obtained, a trained fieldworker conducted the VASA interview. There were five identified pregnancy-related deaths during 2011, with ages ranging from 15–39. Clinical staff members caring for women whose deaths occurred at the hospital were not formally interviewed as part of the VASA process. Some of these staff may have later participated in hospital quality review sessions and subsequent quality improvement efforts. CHWs, local MVP site team health staff, community members, hospital staff, government health staff, West Africa Regional MVP staff, and New Yorkbased MVP staff were all involved at various levels of analysis within the rapid cycle of change review process. Within the MVP area, CHWs are each assigned approximately 120 households, for which they provide basic and preventative household-level healthcare. Initially using paper-based data collection and management tools, CHWs transitioned to mHealth platform Childcount + in 2010. ChildCount + was developed by MVP to empower communities to improve child and maternal survival [26]. The platform uses SMS text messages or smartphone applications to facilitate and coordinate the activities of CHWs. Childcount + modules can be used for screening and treating common conditions of diarrhoeal disease, febrile illness and acute malnutrition, as well as vital statistics data collection, and monitoring of CHW workload and performance. The database includes built-in reminders, allowing CHWs to track antenatal and postnatal care visits, growth monitoring and immunization coverage within their households [27]. CHW managers monitored Childcount + data across the site with support from the wider health team and regional MVP staff. Periodic retraining of CHWs was conducted to optimize accuracy of data collection. All households within the study site were registered with Childcount+, allowing reliable tracking of pregnancies, births and deaths. Death notification was done by CHWs immediately after identifying a death. For all deaths of children under five and women of aged 12-49 yo, a fieldworker trained to handle the sensitive nature of death and mourning in a culturally appropriate manner visited the household approximately one to two weeks after a death to meet with a family member or primary caregiver. If the caregiver gave consent, the fieldworker conducted a detailed interview using the MVP standardized VASA tool. The MVP VASA tool is based on the WHO verbal autopsy questionnaire, referred to by Leitao et al. [13], but with some modifications that include an expanded section exploring social contributors to mortality such as access to transport. Information collected included demographic profile of the pregnant woman and interviewee, experiences of the woman during pregnancy such as health events and healthcare sought and used, and social circumstances around the death. During 2011, the site was transitioning from paper to mHealth collection of VASA so data were collected either by hand or using a mobile device. Regardless of collection method, data was subsequently uploaded to a central database. Once data had been uploaded, a pre-set algorithm was used to determine probable cause of death and contributing social factors. Clinical and project staff were able to access the completed questionnaire via the database at any time for clarification or further detailed analysis including age, place of death, gestation and parity. As part of the MVP quality improvement efforts, site teams are encouraged to review routinely collected health data, and to conduct monthly morbidity and mortality meetings in which de-identified VASA findings are discussed alongside other routine health indicators such as childhood growth monitoring. Input from community members is sought at these meetings. Trends of maternal deaths over time are collated on an annual and semi-annual basis, and outcomes across multiple sites reviewed by regional MVP staff. Through these routine processes, it was noted in mid-2011 there seemed to be an increase in maternal deaths in our study site. Health team staff revisited the cases and reviewed the circumstances around these deaths in more detail. The dual system of paper and electronic versions of VASA and algorithm results were crosschecked and verified by clinicians for plausibility and accuracy. Clarification was sought from CHWs, clinic staff or household members if required. Each case of maternal death was summarized and tabulated to examine patterns. Once it was determined that preventable factors may have been involved in several maternal deaths, a comprehensive report was put together. Identifiers were removed to ensure anonymity of the presented data, given the sensitive nature of this case study. This report was then presented to target audiences on the basis of need and relevance, largely involving individuals or groups most likely to redress problems, such as the regional medical director, hospital director and hospital clinical manager. Other members of the multidisciplinary MVP intervention team were also provided with summary results for input. Feedback from these meetings was captured as part of the quality improvement cycle, and for future reference and utilization. The process was deliberately reiterative, with multiple cycles of review and analysis, and new data added as it became available. Ethical approval for data collection using Childcount + and VASA tools was granted by the National Ethics Committee for Health Research in Senegal (Senegal Comite National d’Ethique pour la Recherche en Sante – NERS). Permission for the study was also obtained from the Ministry of Health (Direction de la Sante) in Senegal. Written informed consent was sought from all VASA interviewees prior to data collection. However, consent for publication of raw data was not obtained from caregivers of women tracked following maternal deaths, because data was collected as part of routine care. Furthermore, publication of the dataset presents minimal risk to confidentiality of study participants because the dataset has been rendered anonymous.

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

1. Implementing routine community-level information systems: This involves using technology, such as mHealth platforms, to track pregnancies, births, and deaths at the household level. This allows for real-time monitoring and detection of maternal deaths, as well as the identification of clinical and social factors contributing to mortality.

2. Integrating verbal and social autopsy: Verbal and social autopsy involves conducting interviews and collecting data from family members or primary caregivers following a maternal death. This helps to uncover the causes and contributing factors of the death, which can then be used to inform targeted interventions and improve the quality of care.

3. Rapid-cycle quality improvement interventions: By using mHealth data collection tools that are sensitive enough to detect small changes in community-level mortality trends in real-time, healthcare providers can implement rapid-cycle quality improvement interventions. This involves continuously monitoring and analyzing data to identify areas of inefficiency and implement timely interventions to address them.

4. Strengthening the health system infrastructure: Expanding coverage of health facilities, such as health posts and primary healthcare clinics, is essential for improving access to maternal health services. This includes ensuring that these facilities are staffed by trained healthcare professionals and equipped to provide comprehensive care, including facility-based deliveries.

5. Establishing a referral network: Setting up an effective referral network is crucial for ensuring a continuum of care for pregnant women. This involves providing transportation options, such as ambulances, to link households, health posts, and primary healthcare clinics to referral hospitals. This helps to ensure that women can access the necessary care in a timely manner.

6. Empowering community health workers (CHWs): CHWs play a vital role in providing basic and preventative healthcare at the household level. By equipping them with mHealth tools, such as SMS text messages or smartphone applications, they can effectively track pregnancies, births, and deaths, as well as monitor antenatal and postnatal care visits, growth monitoring, and immunization coverage within their assigned households.

These innovations, when implemented together, can help improve access to maternal health by enhancing surveillance systems, identifying barriers within the care continuum, and facilitating rapid-cycle quality improvement interventions.
AI Innovations Description
The recommendation that can be developed into an innovation to improve access to maternal health based on the case study in Senegal is the implementation of a routine community-level information system that integrates household-based death notification, verbal autopsy, and social autopsy. This system would involve community health workers conducting regular household visits to track pregnancies, births, and deaths, and promptly notifying and investigating any maternal deaths that occur. The use of mHealth data collection tools, such as the Childcount+ platform, would enable real-time tracking of maternal mortality trends and facilitate rapid-cycle quality improvement interventions. Additionally, the system would involve the establishment of social accountability structures, such as mortality reviews, to identify and address barriers within the continuum of maternal care. By implementing this innovative approach, healthcare providers and policymakers can gain valuable insights into the causes of maternal deaths and develop targeted interventions to improve the quality of care and access to maternal health services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthen referral systems: Enhance the existing referral network by improving transportation services and ensuring timely access to higher-level healthcare facilities.

2. Enhance postpartum care: Focus on improving the management of postpartum complications, such as postpartum hemorrhage, at healthcare facilities to reduce maternal mortality rates.

3. Increase community-level surveillance: Expand the use of community health workers (CHWs) to track pregnancies, births, and deaths at the household level. This can help identify barriers within the continuum of maternal care and enable timely interventions.

4. Implement mHealth data collection tools: Utilize mobile health (mHealth) platforms, such as Childcount+, to facilitate data collection and management by CHWs. These tools can improve the accuracy and efficiency of tracking maternal health indicators.

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

1. Baseline data collection: Gather data on maternal mortality rates, referral systems, postpartum care, and existing community-level surveillance systems.

2. Define indicators: Identify key indicators to measure the impact of the recommendations, such as maternal mortality rates, referral utilization rates, and coverage of postpartum care.

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 size, healthcare infrastructure, and resource availability.

4. Input data for the recommendations: Input data on the implementation of the recommendations into the simulation model. This could include changes in referral system infrastructure, training programs for healthcare providers, and the deployment of CHWs for community-level surveillance.

5. Run simulations: Run the simulation model multiple times to assess the potential impact of the recommendations on improving access to maternal health. Analyze the results to determine changes in maternal mortality rates, referral utilization rates, and other relevant indicators.

6. Evaluate and refine: Evaluate the simulation results and refine the recommendations if necessary. Consider factors such as cost-effectiveness, feasibility, and scalability of the interventions.

7. Communicate findings: Present the simulation findings to stakeholders, policymakers, and healthcare providers to inform decision-making and prioritize interventions for improving access to maternal health.

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

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