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Evaluation of the use of modelling in resource allocation decisions for HIV and TB.

Bowring AL, Ten Brink D, Martin-Hughes R, Fraser-Hurt N, Cheikh N, Scott N.

  • Published 16 Jan 2024

  • Volume 9

  • ISSUE 1

  • Pagination e012418

  • DOI 10.1136/bmjgh-2023-012418.


Introduction: Globally, resources for health spending, including HIV and tuberculosis (TB), are constrained, and a substantial gap exists between spending and estimated needs. Optima is an allocative efficiency modelling tool that has been used since 2010 in over 50 settings to generate evidence for country-level HIV and TB resource allocation decisions. This evaluation assessed the utilisation of modelling to inform financing priorities from the perspective of country stakeholders and their international partners.

Methods: In October to December 2021, the World Bank and Burnet Institute led 16 semi-structured small-group virtual interviews with 54 representatives from national governments and international health and funding organisations. Interviews probed participants' roles and satisfaction with Optima analyses and how model findings have had been used and impacted resource allocation. Interviewed stakeholders represented nine countries and 11 different disease programme-country contexts with prior Optima modelling analyses. Interview notes were thematically analysed to assess factors influencing the utilisation of modelling evidence in health policy and outcomes.

Results: Common influences on utilisation of Optima findings encompassed the perceived validity of findings, health system financing mechanisms, the extent of stakeholder participation in the modelling process-including engagement of funding organisations, sociopolitical context and timeliness of the analysis. Using workshops can facilitate effective stakeholder engagement and collaboration. Model findings were often used conceptually to localise global evidence and facilitate discussion. Secondary outputs included informing strategic and financial planning, funding advocacy, grant proposals and influencing investment shifts.

Conclusion: Allocative efficiency modelling has supported evidence-informed decision-making in numerous contexts and enhanced the conceptual and practical understanding of allocative efficiency. Most immediately, greater involvement of country stakeholders in modelling studies and timing studies to key strategic and financial planning decisions may increase the impact on decision-making. Better consideration for integrated disease modelling, equity goals and financing constraints may improve relevance and utilisation of modelling findings.