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

Cohen JM, Singh I, O'Brien ME - Malar. J. (2008)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinton Foundation HIV/AIDS Initiative, Center for Strategic HIV Operations Research, 383 Dorchester Avenue, Suite 400, Boston, MA 02127, USA. jcohen@clintonfoundation.org

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.

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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.
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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.

Mentions: Using these models, disbursement slopes and intercepts were predicted for the 33 grants in the validation set. These predictions were compared against the smoothed actual disbursement lines and the disbursements planned in the grant application (Figure 4). Predicted yearly global estimates averaged an error of +/- $3.05 M (6.7%) and were significantly correlated with the fit lines (r = 0.998, p < 0.0001). Yearly error ranged from $1.0 M (1.1%) in 2005 to $4.9 M (20.6%) in 2003. Quarterly percent error was higher than yearly error, with an average of +/- $875,503 (11.7%) per quarter, but quarterly estimates were still highly and significantly correlated with fit lines (r = 0.987, p < 0.0001). Quarterly estimates from the simple summation of planned funding were also significantly correlated with smoothed quarterly disbursements (r = 0.919, p < 0.0001), but were less accurate than the prediction model with an average error of +/- $7.0 M (43.5%) per quarter and +/- $28.1 M per year (40.9%).


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

Cohen JM, Singh I, O'Brien ME - Malar. J. (2008)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2570684&req=5

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.
Mentions: Using these models, disbursement slopes and intercepts were predicted for the 33 grants in the validation set. These predictions were compared against the smoothed actual disbursement lines and the disbursements planned in the grant application (Figure 4). Predicted yearly global estimates averaged an error of +/- $3.05 M (6.7%) and were significantly correlated with the fit lines (r = 0.998, p < 0.0001). Yearly error ranged from $1.0 M (1.1%) in 2005 to $4.9 M (20.6%) in 2003. Quarterly percent error was higher than yearly error, with an average of +/- $875,503 (11.7%) per quarter, but quarterly estimates were still highly and significantly correlated with fit lines (r = 0.987, p < 0.0001). Quarterly estimates from the simple summation of planned funding were also significantly correlated with smoothed quarterly disbursements (r = 0.919, p < 0.0001), but were less accurate than the prediction model with an average error of +/- $7.0 M (43.5%) per quarter and +/- $28.1 M per year (40.9%).

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinton Foundation HIV/AIDS Initiative, Center for Strategic HIV Operations Research, 383 Dorchester Avenue, Suite 400, Boston, MA 02127, USA. jcohen@clintonfoundation.org

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.

Show MeSH
Related in: MedlinePlus