Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

Abstract

Background: An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT) and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding.

Methods: Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries.

Results: Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p < 0.0001). Additionally, predicted ACT procurement, extrapolated from forecasted disbursements, was correlated strongly with actual ACT procurement supported by two grants from the Global Fund's first (r = 0.945, p < 0.0001) and fourth (r = 0.938, p < 0.0001) funding rounds.

Conclusion: This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability.

Figure 1

Fitting disbursement slopes and intercepts. Depiction of the ordinary least-squares regression used to calculate the disbursement slope and intercept for a hypothetical Global Fund grant.

Figure 2

Diagram of split-sample model fitting methodology.

Figure 3

Smoothed quarterly disbursements versus actual quarterly disbursements. Comparison of smoothed quarterly approximations from regressing cumulative disbursements against time and actual quarterly disbursements on all Global Fund malaria grants from funding Round 1–6.

Figure 4

Predicted quarterly disbursements. Comparison of quarterly disbursements on 33 Global Fund malaria grants in the validation dataset as (1) calculated from the actual smoothed slopes, (2) estimated from predictive models, and (3) summed from disbursement planned in grant proposals.

Figure 5

Prediction of ACT procurement in two countries. Validation of predicted disbursements and ACT procurement for (a) a Round 4 grant to a country in East Africa and (b) a Round 1 grant to a country in Southern Africa with actual disbursement and ACT importation records.