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

PLoS One. 2021 Oct 25;16(10):e0258354. doi: 10.1371/journal.pone.0258354. eCollection 2021.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Child Mortality / trends
  • Child, Preschool
  • Ethiopia
  • Humans
  • Infant
  • Infant, Newborn
  • Management Information Systems*
  • Models, Theoretical*
  • Mozambique
  • Organization and Administration
  • Tanzania

Grants and funding

This research was funded by the Bill & Melinda Gates Foundation ( through the grant award INV-0077638 received by SS Gilbert. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.