Evaluation of the Saving Little Lives Program Scale-Up: Implementation of an Evidence-Based Minimum Care Package for Preterm and Low-Birth-Weight Newborns in Ethiopia
Year: 2026
Type: article
Abstract: Background: In 2019, Ethiopia’s neonatal mortality rate was 33 deaths per 1000 live births, far from the Sustainable Development Goal 3.2 of 12 or fewer deaths per 1000 live births by 2030. With funding from the Global Financing Facility, the Ministry of Health designed the Saving Little Lives (SLL) program in collaboration with local universities to address the gap. The SLL program aimed to decrease 28-day neonatal mortality by 35% by implementing evidence-based Minimum Care Package (MCP) interventions in 290 hospitals in Ethiopia, prioritizing preterm and low-birth-weight newborns. Methods: From June 2021 to May 2024, we conducted a pragmatic before-and-after evaluation within the planned non-randomized stepped-wedge implementation study, including 290 hospitals in four regions of Ethiopia. We selected 36 primary hospitals as evaluation sites to compare neonatal mortality rate (NMR) before and after the implementation of the SLL program. The implementation occurred in three phases, with Regional Health Bureaus (RHBs) purposively selecting SLL-supported hospitals. The SLL MCP interventions consisted of strengthening care at the labor and delivery (LD) unit, including preparation for birth and essential newborn care components, including resuscitation for asphyxiated newborns, CPAP initiation at birth when indicated, and safe referral of small and sick neonates. At the Neonatal Intensive Care Unit (NICU), interventions included CPAP for respiratory distress syndrome, sepsis management, thermal care, and feeding management. At the Kangaroo Mother Care (KMC) Unit, interventions included skin-to-skin care and exclusive breastfeeding for preterm or LBW newborns. We strengthened care through hands-on training, mentoring, provision of essential equipment and devices, and infrastructure improvements. After addressing missing values using multiple imputations, we applied descriptive statistics to estimate the neonatal mortality rate (NMR), defined as the 28-day mortality among newborns born in or admitted to participating primary hospitals per 1000 live births, before and after the SLL MCP implementation, and a mixed-effects logistic regression model to estimate the pooled mean predicted probability of mortality before and after the program’s implementation. Results: We recorded 63,624 live births across the 36 evaluation hospitals; 59,086 (92.9%) were inborn (born in the same hospital), and 4538 (7.1%) were outborn (born elsewhere and admitted to the evaluation hospitals). We excluded 31,839 live births (50.0%) (all live births from 16 of the 36 hospitals) from the final analysis because they lacked observations in the pre- or post-intervention period. In nine hospitals, the Ministry of Health (MoH) and RHBs initiated the interventions before baseline measurements were taken. In three hospitals in Tigray, the war in the region suspended implementation and evaluation activities, leading to missing baseline evaluation data. Post-intervention data could not be collected at two hospitals in Amhara due to armed conflict, and at two hospitals in the Southern Nations, Nationalities, and Peoples (SNNP) region because research staff left the team. The predicted neonatal mortality rate using data from 20 primary hospitals with 31,785 mother-baby dyads was significantly reduced from 42.9 (95% CI: 34.2–51.6) per 1000 live births during the pre-intervention period to 30.2 (95% CI: 24.2–36.5) per 1000 live births during the post-intervention period (adjusted relative risk of 0.71; 95% CI: 0.58–0.84), showing a 29% reduction in neonatal mortality. Conclusions: The SLL program implemented a large-scale and complex package of interventions in a resource-limited setting and was associated with a significant reduction in neonatal mortality.
Source: Children
Authors: Abiy Seifu Estifanos, Berhan Tassew Woldehana, Mekdes Shifeta Argaw, Abebe Gebremaraim Gobezayehu, Lamesgin Alamineh Endalamaw, Abebech Demissie Aredo, Znabu Hadush Kahsay, Kehabtimer Shiferaw Kotiso, Meles Solomon Mehari, Dereje Duguma, Samson Yohannes Amare, Yayeh Negash, Tiyese Chimuna, Damen Hailemariam, Akalewold Alemayehu, Gurmesa Tura Debelew, Siren Rettedal, Araya Abrha Medhanyie
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225773581