Impact of the training, support and access model (TSAM) on patient health outcomes in Rwanda: controlled longitudinal study
Bibliographic record
Abstract
Abstract Achieving maternal and newborn health (MNH) related Sustainable Development Goal targets will require high-quality health systems in low– and middle-income countries. While over 90% of deliveries in Rwanda take place in health facilities, maternal and neonatal mortality rates remain high. In an effort to bolster quality of care provided to women and newborns to ultimately reduce morbidity and mortality, the Training, Support and Access Model (TSAM) clinical mentorship was established in 10 district hospitals in Rwanda in 2017. We evaluated the impact of the TSAM clinical mentorship intervention on maternal and newborn health outcomes. We used monthly time series data from the DHIS2-enabled Rwanda health management information system from February 2014 to February 2020 to assess the impact of the TSAM intervention on outcomes of care for MNH in intervention hospitals relative to concurrent control hospitals. Using a controlled quasi-experimental interrupted time series analysis, we estimated changes in rates of inpatient mortality and morbidity for MNH associated with the implementation of the TSAM clinical mentorship. The study cohort included 25 hospitals (10 TSAM hospitals and 15 control hospitals) that collectively reported 339,850 hospital deliveries and 94,584 neonatal hospital admissions. We found that the implementation of the TSAM clinical mentorship intervention was associated with a two-years reduction of 84% in the obstetrical complication case fatality rate, 32% in hospital neonatal mortality rate, 30% in postpartum hemorrhage incidence rate, and 48% in neonatal asphyxia incidence rate in TSAM hospitals relative to control hospitals. However, the stillbirth rate did not decline following the TSAM intervention. We found that a quality improvement strategy that employed continuous quality improvement approaches using onsite clinical mentorship of health providers along with involvement of health facility leadership to facilitate the improvement was associated with improvements in MNH in Rwanda. Our findings provide evidence that can justify the scale up of TSAM across the country and potentially in other similar settings. Summary box What is already known? Poor quality of healthcare is currently a bigger driver of excess maternal and neonatal mortality than under-utilization of health facilities in many low– and middle-income countries (LMICs). Achieving maternal and newborn health related Sustainable Development Goal targets will require high-quality health systems in LMICs. What does this study add? The Training, Support and Access Model (TSAM) clinical mentorship implemented in 10 Rwandan district hospitals to bolster quality of care provided to women and newborns was associated with a reduction in in-hospital maternal and newborn deaths. However, the (intrapartum) stillbirth rate did not decline following the TSAM intervention. The TSAM intervention was associated with a significant decline in in-hospital maternal and neonatal morbidity (e.g., incidence of postpartum hemorrhage and neonatal asphyxia). What do the new findings imply? Employing continuous quality improvement approaches using onsite clinical mentorship of health providers along with involvement of health facility leadership to facilitate the improvement can be an effective strategy to improve maternal and newborn health outcomes. Quasi-experimental methods leveraging routine health information systems data can be useful to study impact of health system improvement interventions in low-resource settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".