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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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Diagram of split-sample model fitting methodology.
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Figure 2: Diagram of split-sample model fitting methodology.

Mentions: Predictive regression models may include variables that are not statistically significant and exclude others that are, since statistical association does not necessarily indicate useful predictive value [16]. To prevent over-fitting of models to the training dataset and to ensure that the variables included in models were not merely statistically significant but also demonstrated predictive ability [17,18], models were constructed using a repeated split-sample procedure (Figure 2). Only those grants for which the Global Fund had made at least three prior funding disbursements to the recipient country (n = 130) were used to fit and test the predictive models, since initial disbursements might not be indicative of future performance. A randomly selected 75% (n = 97) of these Round 1–6 grants were used to fit and compare models constructed with candidate variables (the "derivation group"), while the remaining 25% of grants (n = 33) were set aside during model fitting to permit validation on data not used to fit the model (the "validation group").


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

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

Diagram of split-sample model fitting methodology.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Diagram of split-sample model fitting methodology.
Mentions: Predictive regression models may include variables that are not statistically significant and exclude others that are, since statistical association does not necessarily indicate useful predictive value [16]. To prevent over-fitting of models to the training dataset and to ensure that the variables included in models were not merely statistically significant but also demonstrated predictive ability [17,18], models were constructed using a repeated split-sample procedure (Figure 2). Only those grants for which the Global Fund had made at least three prior funding disbursements to the recipient country (n = 130) were used to fit and test the predictive models, since initial disbursements might not be indicative of future performance. A randomly selected 75% (n = 97) of these Round 1–6 grants were used to fit and compare models constructed with candidate variables (the "derivation group"), while the remaining 25% of grants (n = 33) were set aside during model fitting to permit validation on data not used to fit the model (the "validation group").

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