HIV prevention costs and program scale: data from the PANCEA project in five low and middle-income countries.
BACKGROUND
Economic theory and limited empirical data suggest that costs per unit of HIV prevention program output (unit costs) will initially decrease as small programs expand. Unit costs may then reach a nadir and start to increase if expansion continues beyond the economically optimal size. Information on the relationship between scale and unit costs is critical to project the cost of global HIV prevention efforts and to allocate prevention resources efficiently.


METHODS
The "Prevent AIDS: Network for Cost-Effectiveness Analysis" (PANCEA) project collected 2003 and 2004 cost and output data from 206 HIV prevention programs of six types in five countries. The association between scale and efficiency for each intervention type was examined for each country. Our team characterized the direction, shape, and strength of this association by fitting bivariate regression lines to scatter plots of output levels and unit costs. We chose the regression forms with the highest explanatory power (R2).


RESULTS
Efficiency increased with scale, across all countries and interventions. This association varied within intervention and within country, in terms of the range in scale and efficiency, the best fitting regression form, and the slope of the regression. The fraction of variation in efficiency explained by scale ranged from 26-96%. Doubling in scale resulted in reductions in unit costs averaging 34.2% (ranging from 2.4% to 58.0%). Two regression trends, in India, suggested an inflection point beyond which unit costs increased.


CONCLUSIONS
Unit costs decrease with scale across a wide range of service types and volumes. These country and intervention-specific findings can inform projections of the global cost of scaling up HIV prevention efforts.
Marseille
Elliot
E
Institute of Health Policy Studies, University of California, San Francisco, USA. emarseille@comcast.net
Dandona
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Marshall
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Saba
Joseph
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Lioznov
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Krupitsky
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Stanley
Nicci
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Peryshkina
Alena
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Kumar
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Muyingo
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D003362:Cost-Benefit Analysis; D003625:Data Collection; D019049:Developed Countries / Q000191:economics*; D003906:Developing Countries / Q000191:economics*; D017598:Efficiency, Organizational / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D015658:HIV Infections / Q000191:economics / Q000453:epidemiology / Q000517:prevention & control*; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D007182:Income / Q000145:classification; D007194:India / Q000453:epidemiology; D008297:Male; D008800:Mexico / Q000453:epidemiology; D017059:Models, Econometric; D010865:Pilot Projects; D011314:Preventive Health Services / Q000191:economics* / Q000458:organization & administration / Q000706:statistics & numerical data; D016730:Program Development; D015397:Program Evaluation; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D012426:Russia / Q000453:epidemiology; D013019:South Africa / Q000453:epidemiology; D014454:Uganda / Q000453:epidemiology