Estimation of health impact from digitalizing last-mile Logistics Management Information Systems (LMIS) in Ethiopia, Tanzania, and Mozambique: A Lives Saved Tool (LiST) model analysis

Abstract

Background: Digital health has become a widely recognized approach to addressing a range of health needs, including advancing universal health coverage and achieving the Sustainable Development Goals. At present there is limited evidence on the impact of digital interventions on health outcomes. A growing body of peer-reviewed evidence on digitalizing last-mile electronic logistics management information systems (LMIS) presents an opportunity to estimate health impact.

Methods: The impact of LMIS on reductions in stockouts was estimated from primary data and peer-reviewed literature, with three scenarios of impact: 5% stockout reduction (conservative), 10% stockout reduction (base), and 15% stockout reduction (optimistic). Stockout reduction data was inverted to stock availability and improved coverage for vaccines and essential medicines using a 1:1 conversion factor. The Lives Saved Tool (LiST) model was used to estimate health impact from lives saved in newborns and children in Mozambique, Tanzania, and Ethiopia between 2022 and 2026 across the three scenarios.

Results: Improving coverage of vaccines with a digital LMIS intervention in the base scenario (conservative, optimistic) could prevent 4,924 (2,578-6,094), 3,998 (1,621-4,915), and 17,648 (12,656-22,776) deaths in Mozambique, Tanzania, and Ethiopia, respectively over the forecast timeframe. In addition, scaling up coverage of non-vaccine medications could prevent 17,044 (8,561-25,392), 21,772 (10,976-32,401), and 34,981 (17,543-52,194) deaths in Mozambique, Tanzania, and Ethiopia, respectively. In the base model scenario, the maximum percent reduction in deaths across all geographies was 1.6% for vaccines and 4.1% for non-vaccine medications.

Interpretation: This study projects that digitalization of last-mile LMIS would reduce child mortality by improving coverage of lifesaving health commodities. This analysis helps to build the evidence base around the benefits of deploying digital solutions to address health challenges. Findings should be interpreted carefully as stockout reduction estimates are derived from a small number of studies.

Fig 1. Impact model connecting digital health and improved health outcomes through LMIS.

Abbreviations: LiST, Lives Saved Tool.

Fig 2. Lives Saved Tool (LiST) impact calculation.

Fig 3. Total lives saved for newborns and children 0–59 months over five years when scaling up coverage of commodities across Mozambique, Tanzania, and Ethiopia.