Learning from changes concurrent with implementing a complex and dynamic intervention to improve urban maternal and perinatal health in Dar es Salaam, Tanzania, 2011-2019

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Study Justification:
– Rapid urbanization in Dar es Salaam has led to congested health facilities, poor quality care, and high maternal and perinatal mortality rates.
– The study aimed to implement a complex and dynamic intervention to improve the quality of care and survival during pregnancy and childbirth in 22 public health facilities in Dar es Salaam.
– The intervention was designed to address gaps in the maternal and perinatal continuum of care, including training, infrastructure improvement, and data quality strengthening.
Study Highlights:
– Significant improvements were observed in the 22 health facilities, including a 41% reduction in congestion, increased use of lower-level facilities, a sixfold increase in quality of care, and reductions in facility-based maternal mortality ratio (47%) and stillbirth rate (19%).
– The intervention was a collaborative effort involving the local government, a non-governmental organization, and multiple partners.
– The intervention was sustained over a decade and leveraged existing structures in the urban health system.
Study Recommendations:
– The study recommends the continuation and expansion of the complex intervention to further improve maternal and perinatal health in Dar es Salaam.
– The findings suggest the need for ongoing training, infrastructure improvement, and data quality strengthening in health facilities.
– The study highlights the importance of strong leadership and coordination at the regional and municipal levels to ensure the success of the intervention.
Key Role Players:
– Local government authorities
– Comprehensive Community Based Rehabilitation in Tanzania (CCBRT)
– Non-governmental organizations (NGOs)
– Regional and municipal health offices
– Health facility managers and staff
– District and regional managers
Cost Items for Planning Recommendations:
– Training programs for healthcare providers
– Infrastructure improvement (building operating theaters, improving flows and privacy)
– Essential equipment and supplies
– Printing of Health Management Information tools
– Staff training on data utilization
– Quality assessments and feedback mechanisms
– Coordination and leadership support at regional and municipal levels
Please note that the cost items provided are general categories and not actual cost estimates.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it presents significant improvements in the health facilities and outcomes. However, to improve the rating, the abstract could provide more specific details about the intervention components, data collection methods, and statistical analysis used.

Introduction Rapid urbanisation in Dar es Salaam, the main commercial hub in Tanzania, has resulted in congested health facilities, poor quality care, and unacceptably high facility-based maternal and perinatal mortality. Using a participatory approach, the Dar es Salaam regional government in partnership with a non-governmental organisation, Comprehensive Community Based Rehabilitation in Tanzania, implemented a complex, dynamic intervention to improve the quality of care and survival during pregnancy and childbirth. The intervention was rolled out in 22 public health facilities, accounting for 60% of the city’s facility births. Methods Multiple intervention components addressed gaps across the maternal and perinatal continuum of care (training, infrastructure, routine data quality strengthening and utilisation). Quality of care was measured with the Standards-Based Management and Recognition tool. Temporal trends from 2011 to 2019 in routinely collected, high-quality data on facility utilisation and facility-based maternal and perinatal mortality were analysed. Results Significant improvements were observed in the 22 health facilities: 41% decongestion in the three most overcrowded hospitals and comparable increase in use of lower level facilities, sixfold increase in quality of care, and overall reductions in facility-based maternal mortality ratio (47%) and stillbirth rate (19%). Conclusions This collaborative, multipartner, multilevel real-world implementation, led by the local government, leveraged structures in place to strengthen the urban health system and was sustained through a decade. As depicted in the theory of change, it is highly plausible that this complex intervention with the mediators and confounders contributed to improved distribution of workload, quality of maternity care and survival at birth.

DSM is one of the fastest growing cities in sSA. With a population approximating six million in 2019, it is expected to become a megacity with over ten million inhabitants by 2030.13 An increasing ratio of births over deaths, migration and administrative reclassification of city boundaries all contribute to this increase.2 DSM scores lowest among African cities on the Urban Health Index, which includes indicators of access to water and sanitation, use of solid fuels, women’s education, women’s knowledge of HIV and child healthcare coverage.2 While most wealth in Tanzania is concentrated in cities, wealth inequality is higher in urban than in rural areas.2 Additional relevant outer and inner contextual factors are represented in figure 1.2 5 14 15 Contextual factors in Dar es Salaam (2010–2019) that may have influenced outcomes of implementation. CCBRT, Comprehensive Community Based Rehabilitation in Tanzania; CDC, Center for Disease Control; GoT, Government of Tanzania; Jhpiego, Johns Hopkins Program for International Education in Gynecology and Obstetrics; MDG, Millennium Development Goals; MDH, Management and Development for Health; NGO, non-governmental organisation; SDG, Sustainable Development Goals. With more than 90% of births in DSM occurring in public facilities, this maternal and newborn intervention focused on strengthening the public maternity care system.11 16 17 The 22 HFs were enrolled into the intervention in three phases. The initial phase started in 2010 and consisted of support to eight HFs (three high-volume hospitals and five health centres), identified in the baseline assessment.11 As additional funding was secured, the programme expanded in two additional phases (2011, 2013) to support a total of 22 HFs (figure 2A), which collectively assisted more than 60% of all facility births in DSM during the 10 years studied (figure 2B). (A) Study setting: map of DSM region indicating the 22 HFs in the 5 municipalities of Dar es Salaam. The intervention programme’s phase 1 (2010) included facilities numbered 1–8, phase 2 (2011) included facilities numbered 9–16, and 17–22 were added in phase 3 (2013). In 2010, the region had three municipalities (Ilala, Kinondoni and Temeke), but in 2016 Kigamboni was carved out of Temeke municipality and Ubungo subdivided from Kinondoni. Population estimates derived from Tanzania’s National Bureau of Statistics’ 2019 projections. (B) Total births (TB) in Dar es Salaam and Total Births in the 22 health facilities supported by the urban maternal and newborn healthcare intervention (2010–2019) (data source: DHIS2). MDH supported 15 HFs. The remainder of births (~10%) occur in private institutions and other public dispensaries. CCBRT, Comprehensive Community Based Rehabilitation in Tanzania; DSM, Dar es Salaam; HF, health facility; MDH, Management and Development for Health. The programme for strengthening maternal and newborn care in DSM was co-designed by the DSM regional health authorities and CCBRT, with additional contributions from multiple partners (online supplemental file 1). The specific components of the complex health system intervention were selected in accordance with the needs revealed in a baseline assessment in 8 of the 22 HFs,11 and formulated as a theory of change (figure 3).18 Theory of change for the complex urban maternal and perinatal healthcare intervention in Tanzania: implementation strategy and interventions. CCBRT, Comprehensive Community Based Rehabilitation in Tanzania; CEmONC, Comprehensive Emergency Obstetric and Neonatal Care; EmONC, Emergency Obstetric and Neonatal Care; RRH, regional referral hospitals. bmjgh-2020-004022supp001.pdf The theory of change was that some of the positive changes in quality of care and survival during birth would be directly related to strengthened competencies of health providers (clinical training, introducing a standards-based approach to care and supervision) and stable access to essential equipment, medicine, emergency surgery and blood transfusion. Other parts of change would be indirectly stimulated through improved data quality and utilisation (audits and quality improvement meetings) and decongestion of the overcrowded municipal hospitals (improved referral system with redistribution of births to upgraded primary level HFs). Evidence-based best practices for each intervention component were extracted and adapted from the national strategic plans and reviews of international scientific literature.19 20 The intervention was adjusted over time in response to successes and challenges, with new elements systematically layered on as new gaps emerged. The core components of the complex intervention are described here, and more details are available in box 1 and online supplemental file 2. Note: The year refers to the year that particular component was first introduced into the programme. See online supplemental files 2–4 for more details on frequency. BEmONC, Basic Emergency Obstetric and Newborn Care; CCBRT, Comprehensive Community Based Rehabilitation in Tanzania; HFs, health facilities; SBM-R, Standards-Based Management and Recognition. The regional health leadership ensured alignment to national strategic priorities, guided the implementation approach, designated focal persons at regional and municipal levels to support coordination, and led the quarterly improvement meetings. In addition, an initial multistakeholder meeting enabled mapping of partners working in maternal health, further enabling collaboration and synergy. This strengthened the system-level coordination, which was crucial for jointly developing referral criteria for women with high-risk pregnancies, easily allowing inputs from health providers and managers across the HFs. Thereby, timely recognition of clinical risks, rapid resuscitation and referral of women who developed obstetric emergencies were promoted, from primary care HFs to hospitals within the referral network. The 22 HFs were linked through a closed user group, with a phone call to follow up on outcomes of referred women. Selection of the HFs targeted government-owned HFs, with high utilisation and/or those planned for upgrades. Following initial experiences during implementation in 2010 and 2011, the regional authorities led the scale-up to an additional 21 HFs (6 dispensaries supported by CCBRT and 15 dispensaries supported by another NGO, Management and Development for Health). The Standards-Based Management and Recognition approach (SBM-R), a validated performance and quality improvement method, developed by Jhpiego, was implemented collaboratively.21–23 SBM-R quality assessments in each facility were conducted by a team led by the regional nursing officer and consisting of representatives from the regional and municipal health offices, the HFs and CCBRT. Assessors spent 2–3 days in each facility performing direct observations and scoring (expressed in percentages) using the SBM-R tool and provided immediate feedback to the HFs.21–23 Intervention components were derived from the Tanzanian Reproductive, Maternal and Newborn Health roadmap and included interventions known to avert maternal and perinatal deaths across the continuum of pregnancy and childbirth.19 20 Initial training targeted skills in routine intrapartum care and Basic Emergency Obstetric and Newborn Care (BEmONC). As gaps emerged, additional short trainings were added (antenatal care, essential newborn care, postnatal care, neonatal resuscitation, surgical skills, safe anaesthesia, kangaroo care, care for sick newborns, Comprehensive Emergency Obstetric and Newborn Care, referral recognition and management, and data quality and perinatal audits). The focus was on building the competencies of inservice healthcare providers. (online supplemental file 3). Five training modalities were implemented: (1) 2-week BEmONC national training, facilitated by national trainers; (2) 5-day critical BEmONC skills course; (3) 1-day modular course on specific topics addressing gaps detected in perinatal audits; (4) on-site trainer: an experienced nurse or doctor supported by CCBRT was periodically stationed full time for periods from 2 weeks to 1 year in the labour ward of high-volume facilities for on-site coaching and support; and (5) on-the-job coaching (mentors would spend 2–3 days in each of the 22 sites, building skills during routine service provision). Extensive investments were made to create a conducive environment for care through improved infrastructure: building operating theatres, improving flows and privacy, as well as gap filling of essential equipment and supplies. Access to safe provision of blood, anaesthesia and surgery was improved. Initial support was through donor-funded NGOs; however, from 2016 it was supported by the Ministry of Health and Social Welfare through the local government (online supplemental file 4). Strengthening recording, reporting and utilisation of routinely collected facility data was a significant part of the intervention, which included support for printing new Health Management Information tools (HMIS), also called MTUHA (Mfumo wa Taarifa za Uendeshaji Huduma za Afya) in Swahili, as well as facility quality assurance extraction sheets, labour case notes and neonatal admission data collection tools, and training more than 292 staff on utilisation of the tools. Routine facility data were validated by direct counting from registers by district managers and CCBRT. This took effect in 2011. Key indicators were presented in regional quarterly improvement meetings and disseminated to all managers at all levels. Leadership and managers would reflect on key indicators which included quality of care scores (SBM-R), cases referred, routine data, and maternal and perinatal deaths from each of the 22 HFs. These meetings provided an opportunity for identification of deficiencies in clinical skills, developing training plans, following up on HF action plans, improving peer accountability and dynamic adaptation of the programme (figure 4). The process was subsequently used for scale-up of the intervention to additional HFs. Scale-up and dynamic adaptation process with feedback loop for continuous improvement. HF, health facility; SBM-R, Standards-Based Management and Recognition. This was a retrospective analysis of a real-world complex intervention. The Donabedian model for analysing quality of care was applied to verify the programme’s theory of change (figure 3), in that the complex intervention may have contributed to improved distribution of births (structure), quality of care (process) and survival (outcome). Data sources were the 22 HFs’ HMIS from 2011 and onwards. Each month, key birth data were extracted from each HF. Temporal changes in facility births were used to determine redistribution of facility births over time. Total numbers of (live) births, mode of birth, and maternal and perinatal deaths were extracted from each HF. Facility maternal mortality ratios over time included maternal deaths from all causes per 100 000 live births. Fresh and macerated stillbirths were calculated together due to misclassification of fresh stillbirths as macerated in some cases. Total early neonatal deaths that occurred in the HF were counted as well; these included neonatal deaths among inborn newborns and those deaths among sick newborns referred after birth elsewhere. Quality of care was measured using the SBM-R tool in each HF through direct structured observations. Annual SBM-R results from 2010 to 2019 were transcribed from hard copy into an Excel sheet, and the average quality scores of all thematic areas resulted in overall quality scores for each HF, expressed as a percentage. While both antenatal care and BEmONC were measured, for this paper only SBM-R scores for BEmONC are presented. In addition, the number of staff receiving competency training was assessed. Possible confounders and mediators include staffing, staff transition, workload changes, health budget, other clinical training interventions and population changes in DSM. Figure 1 presents some contextual factors. However, time and resources did not allow for detailed analysis of these variables. The study was designed based on a needs assessment done pre-implementation in 2009,11 with the aim to improve service delivery, improve quality of care, and reduce poor maternal and perinatal outcomes. This included patient interviews (not included in this paper). During implementation, community health workers, community leaders and HF governing boards were among the stakeholders engaged and orientated on the programme, the routine outcome data and the intervention, and as end users of the supported HFs provided patient-level perspectives (online supplemental file 3). Data on the quality of care, maternal and perinatal audits were reviewed by health managers in monthly meetings and by district and regional managers in quarterly meetings to ensure safety of women. Data used for analysis in this paper were routine, anonymised facility data. Simple descriptive analyses were conducted using frequencies and percentages. Univariate generalised linear models for Poisson distributions were performed to detect changes over time in SBM-R scores, operative births, and the number of births, stillbirths, and maternal and neonatal deaths. SBM-R scores were analysed according to the phase each hospital first entered the intervention. The number of births was analysed according to the level of facility designated at the beginning of the intervention (hospitals n=3, health centres n=6, dispensaries n=13). Operative births and birth outcomes were analysed across all HFs. Results were reported as rate ratio (RR) with 95% CI, with RR representing the change in rate per year. Analyses were performed in Microsoft Excel (V.2013) and R V.3.5.3 (The R Foundation for Statistical Computing, 2019).24

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

1. Participatory Approach: Implementing a participatory approach that involves collaboration between the regional government and non-governmental organizations can help address gaps in maternal and perinatal care. This approach ensures that multiple stakeholders are involved in decision-making and implementation, leading to more effective and sustainable interventions.

2. Complex and Dynamic Intervention: Implementing a complex and dynamic intervention that addresses various aspects of the maternal and perinatal continuum of care can lead to significant improvements in access to quality care. This can include training programs, infrastructure improvements, data quality strengthening, and utilization strategies.

3. Standards-Based Management and Recognition (SBM-R) Tool: Using a validated performance and quality improvement method like the SBM-R tool can help assess and improve the quality of care in health facilities. This tool involves direct observations and scoring of facility performance, providing immediate feedback and opportunities for improvement.

4. Training Programs: Providing comprehensive training programs for healthcare providers can enhance their competencies in providing routine intrapartum care, emergency obstetric and newborn care, and other essential services. These trainings can be tailored to address specific gaps identified through audits and quality improvement meetings.

5. Infrastructure Improvements: Investing in infrastructure improvements, such as building operating theaters, improving flows and privacy, and filling gaps in essential equipment and supplies, can create a conducive environment for maternal and perinatal care. This ensures that health facilities have the necessary resources to provide safe and effective care.

6. Data Quality and Utilization: Strengthening the recording, reporting, and utilization of routinely collected facility data can provide valuable insights for decision-making and quality improvement. This can involve training staff on data collection tools, validating routine facility data, and conducting regular audits and quality improvement meetings.

7. System-Level Coordination: Establishing strong system-level coordination, led by regional health authorities, can ensure alignment with national strategic priorities and facilitate collaboration among multiple partners. This coordination can help develop referral criteria, improve resuscitation and referral processes, and promote accountability and continuous improvement.

It’s important to note that these recommendations are based on the specific context and interventions described in the provided information. The effectiveness of these innovations may vary depending on the local context and resources available.
AI Innovations Description
The recommendation to improve access to maternal health based on the described intervention in Dar es Salaam, Tanzania is to implement a similar complex and dynamic intervention in other urban areas facing similar challenges. This intervention should focus on addressing gaps across the maternal and perinatal continuum of care, including training, infrastructure improvement, routine data quality strengthening and utilization.

Key components of the intervention should include:

1. Training: Provide comprehensive training to healthcare providers in routine intrapartum care, emergency obstetric and newborn care, antenatal care, essential newborn care, postnatal care, neonatal resuscitation, surgical skills, safe anesthesia, kangaroo care, care for sick newborns, comprehensive emergency obstetric and newborn care, referral recognition and management, and data quality and perinatal audits. The training should focus on building the competencies of in-service healthcare providers.

2. Infrastructure Improvement: Invest in improving the infrastructure of health facilities, including building operating theaters, improving flows and privacy, and filling gaps in essential equipment and supplies. Access to safe provision of blood, anesthesia, and surgery should be improved.

3. Data Quality Strengthening and Utilization: Strengthen the recording, reporting, and utilization of routinely collected facility data. This can be done by providing support for printing new Health Management Information tools, training staff on data utilization, and conducting regular quality assessments using validated tools.

4. Collaboration and Coordination: Foster collaboration and coordination among stakeholders, including regional and municipal health authorities, non-governmental organizations, and other partners. This can be achieved through regular multistakeholder meetings, joint development of referral criteria, and closed user groups for communication and follow-up on referred women.

5. Continuous Improvement: Establish a feedback loop for continuous improvement. This can be done through regular improvement meetings at regional and municipal levels, where key indicators such as quality of care scores, cases referred, routine data, and maternal and perinatal deaths are discussed. These meetings provide an opportunity for identifying deficiencies, developing training plans, improving peer accountability, and adapting the intervention based on feedback.

By implementing a similar intervention in other urban areas, it is expected that access to maternal health will be improved, leading to reduced facility-based maternal and perinatal mortality rates, improved quality of care, and better distribution of workload.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening infrastructure: Improve the physical infrastructure of health facilities by building operating theaters, improving flows and privacy, and filling gaps in essential equipment and supplies. This will create a conducive environment for providing quality maternal health care.

2. Enhancing training and skills development: Provide comprehensive training programs for healthcare providers, focusing on routine intrapartum care, emergency obstetric and newborn care, antenatal care, postnatal care, neonatal resuscitation, surgical skills, safe anesthesia, and other essential skills. This will improve the competencies of healthcare providers and ensure they can deliver high-quality care.

3. Improving data quality and utilization: Implement strategies to strengthen the recording, reporting, and utilization of routinely collected facility data. This includes providing support for printing new Health Management Information tools, training staff on data utilization, and conducting regular audits to ensure data accuracy and quality. This will enable better monitoring and evaluation of maternal health outcomes and facilitate evidence-based decision-making.

4. Decongesting overcrowded hospitals: Develop and implement a referral system that redistributes births from overcrowded hospitals to upgraded primary level health facilities. This will help alleviate the burden on overcrowded hospitals and improve access to maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators that reflect access to maternal health, such as the number of facility-based births, facility-based maternal mortality ratio, stillbirth rate, and quality of care scores.

2. Collect baseline data: Gather baseline data on the identified indicators before implementing the recommendations. This will provide a starting point for comparison and evaluation.

3. Implement recommendations: Roll out the recommended interventions, ensuring proper implementation and monitoring.

4. Collect post-intervention data: Continuously collect data on the identified indicators after implementing the recommendations. This data should cover a sufficient period to capture any changes and trends.

5. Analyze and compare data: Analyze the pre- and post-intervention data to assess the impact of the recommendations. Use statistical methods, such as univariate general linear models, to detect changes over time in the indicators.

6. Calculate rate ratios: Calculate rate ratios with 95% confidence intervals to quantify the changes in the indicators. This will provide a measure of the impact of the recommendations on improving access to maternal health.

7. Interpret and report findings: Interpret the results of the analysis and report the findings, highlighting the improvements in access to maternal health resulting from the implemented recommendations.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and evaluate the effectiveness of the interventions.

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