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

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

N/A

• sequeira dmello, brenda
• sellah, ziada
• magembe, grace
• housseine, natasha
• maaløe, nanna
• van den akker, thomas
• meyrowitsch, dan w.
• mushi, timothy
• masweko, mangalu
• jidayi, dorcas
• van roosmalen, jos
• kidanto, hussein