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Journal of Public Health Management & Practice:
doi: 10.1097/PHH.0b013e3182431da6
Original Articles

Resource and Cost Adjustment in the Design of Allocation Funding Formulas in Public Health Programs

Buehler, James W. MD; Bernet, Patrick M. PhD; Ogden, Lydia L. PhD, MPP

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Abstract

Context: Multiple federal public health programs use funding formulas to allocate funds to states.

Objective: To characterize the effects of adjusting formula-based allocations for differences among states in the cost of implementing programs, the potential for generating in-state resources, and income disparities, which might be associated with disease risk.

Setting: Fifty US states and the District of Columbia.

Intervention: Formula-based funding allocations to states for 4 representative federal public health programs were adjusted using indicators of cost (average salaries), potential within-state revenues (per-capita income, the Federal Medical Assistance Percentage, per-capita aggregate home values), and income disparities (Theil index).

Main Outcome: Percentage of allocation shifted by adjustment, the number of states and the percentage of US population living in states with a more than 20% increase or decrease in funding, maximum percentage increase or decrease in funding.

Results: Each adjustor had a comparable impact on allocations across the 4 program allocations examined. Approximately 2% to 8% of total allocations were shifted, with adjustments for variations in income disparity and housing values having the least and greatest effects, respectively. The salary cost and per-capita income adjustors were inversely correlated and had offsetting effects on allocations. With the exception of the housing values adjustment, fewer than 10 states had more than 20% increases or decreases in allocations, and less than 10% of the US population lived in such states.

Conclusions: Selection of adjustors for formula-based funding allocations should consider the impacts of different adjustments, correlations between adjustors and other data elements in funding formulas, and the relationship of formula inputs to program objectives.

© 2012 Lippincott Williams & Wilkins, Inc.

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