In general, the predicted reduction in expenditures that would occur in the absence of a condition, expressed as a fraction of total expenditures (AF), is not a share of total expenditures associated with each condition. When the presence of one condition affects spending associated with other conditions, condition-specific AFs include expenditures associated with the joint occurrence of that condition and other conditions. If these AFs are summed, a portion of the expenditures associated with the joint occurrence of conditions will be double-counted, and the sum will not give the appropriate combined share of expenditures attributable to the set of conditions.
Commonly used models in health economics imply these types of nonconstant marginal effects, including OLS on log expenditures, nonlinear least squares, and GLM. Therefore, researchers must be careful when interpreting AFs, especially with multiple conditions of interest. AFs indicate the extent to which medical expenditures would be lower in the absence of particular conditions, all else constant.
For researchers interested in dividing existing expenditures into mutually exclusive categories of conditions, we recommend reporting the upper and lower bounds for the AF for each condition described above as well as our complete cross-classification weighting scheme. The bounds are more accurate but less precise; in contrast, the cross-classification method is more precise and allows interpretation of AFs as shares of total expenditures that can be summed to get the total impact of the set of conditions, but relies on additional assumptions.
One limitation of our weighting scheme is that it does not rely on clinical theory to estimate condition-specific expenditures. If condition 1 is only expensive in conjunction with condition 2 but alone is relatively cheap to treat compared with condition 2, our weighting scheme would incorrectly assign most of the expenditures associated with the joint conditions to condition 2. In many applications clinical theory for causal relationships may not be available; in these cases our weighting scheme provides a reasonable alternative. When theory is available, complete cross-classification could be combined with alternative weighting schemes that incorporate clinically meaningful relationships among conditions.
Future research should explore the extent to which the bounds can be improved. It can also build on the intuition underlying the proposed strategies to deal with double counting in other measures of burden (e.g., morbidity, mortality, or productivity) and provide better estimates of the relative burden of conditions.