Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/33364
Title: A decision-tree model to evaluate the impacts of workforce investments aimed at strengthening emergency obstetric and neonatal care (EmONC) facilities in Burundi
Authors: Habonimana, D
Leckcivilize, A
Nicodemo, C
Nzorironkankuze, JB
Ndacayisaba, A
Bishinga, A
Ndayisenga, J
Niane, ESD
Bazikamwe, S
Ndabashinze, P
English, M
Keywords: EmONC;workforce;workload Indicator for Staffing Needs (WISN);decision tree model;Burundi
Issue Date: 29-Jan-2026
Publisher: BioMed Central (part of Springer Nature)
Citation: Habonimana, D. et al. (2026) ‘A decision-tree model to evaluate the impacts of workforce investments aimed at strengthening emergency obstetric and neonatal care (EmONC) facilities in Burundi’, Cost Effectiveness and Resource Allocation, 24, 37, pp. 1–16. doi: 10.1186/s12962-026-00720-5.
Abstract: Introduction: Human resources for health (HRH) in low- and middle income countries (LMICs) are often allocated irrationally based on normative policies that ignore service delivery patterns and health outcomes, failing to account for costs and expected benefits. To help Burundi rationalise scarce resources and tackle health system inefficiencies, we developed four workforce investment scenarios aimed at strengthening emergency obstetric and neonatal care (EmONC) facilities and predicted the costs and benefits associated with these investment proposals over the 2025–2030 timeframe. Methods: We documented across the 112 Burundian EmONC facilities the available human resources and collated annual data on deliveries and obstetric complications covering 2021. Using the Workload Indicator for Staffing Needs (WISN) methodology, we estimated for each facility the workforce deficit and developed four EmONC workforce investment scenarios; no investment, a partial workforce package, and full workforce packages targeted at either all facilities or a select group of priority facilities. A decision tree model helped to predict the costs per maternal death averted. Results: A budget of US$ 11.1 million is required to fully address workforce gaps across the 104 facilities with shortages over five years, potentially saving 532 maternal lives each year. Given budget constraints, Burundi could focus on the 24 priority facilities with an investment of US$ 3.27 million or allocate US$ 3.18 million to supply two midwives across the 104 facilities. These investments are projected to avert 163 and 267 maternal deaths annually, respectively. The partial workforce investment is more cost-effective with an expected expenditure of US$ 2380.58 for each maternal life saved. Conclusion: Efforts to invest across the EmONC network if simply based on assigned B/CEmONC status may poorly allocate scarce resources. If resources are limited, investing in two additional midwives across the 104 EmONC facilities would be more efficient than implementing a full workforce package. However, health benefits of any workforce investment significantly depend on an enabling environment.
Description: Data availability: This study used datasets from the main EmONC facility survey and routine monitoring data which are not publicly available as primarily owned by the Burundian Ministry of Health (MoH) and are bound by a strong data sharing policy which does not permit authors to share them. However, these data can be obtained by sending a reasonable request to the reproductive, maternal, newborn, child, and adolescent’s health programme of the Burundian MoH. The corresponding author can share STATA command files upon request.
Electronic supplementary material is available online at: https://link.springer.com/article/10.1186/s12962-026-00720-5#Sec19 .
URI: https://bura.brunel.ac.uk/handle/2438/33364
DOI: https://doi.org/10.1186/s12962-026-00720-5
Other Identifiers: ORCiD: Catia Nicodemo https://orcid.org/0000-0001-5490-9576
Appears in Collections:Department of Strategy, Entrepreneurship and Management Research Papers *

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