track the flow of funds into and out of the health care system, providing information on payer type (e.g., Medicare, out of pocket) and services purchased (e.g., hospital care, pharmaceuticals) in a series of standardized tables published annually on the CMS website. A typical NHEAs table forms a “sources and uses” matrix, imposing a specific set of accounting principles for who pays and how much, ensuring that all subtotals add up in a consistent manner.

While providing essential information on health care spending trends, the NHEAs have historically revealed little about the output of the sector—what is being bought—in terms that are meaningful for assessing medical care productivity and the impact on population health. The highly aggregated NHEAs data leave gaps that need filling if a number of critical health policy questions are to be resolved. Does an expensive new medical technology provide enough added health benefit to justify its use when compared with less costly alternatives? How do the public and private sectors encourage or limit adoption and diffusion of new technologies? And, more generally, which medical treatments are the most productive in terms of generating improved population health, and which are the least?

With the NHEAs alone, it is not possible to determine whether medical costs are increasing more because of cardiovascular disease treatments or because of cancer prevention activities. It is also largely unknown who is affected, and how, by the spending. Are vulnerable populations benefiting or suffering from current resource allocation strategies? Simply put, health care cost containment strategies in the United States are debated and pursued with inadequate information about what (or on whom) money is being spent (Triplett, 2001; Triplett and Bosworth, 2008). Addressing critical health policy questions requires more disaggregated data. Recognizing this need, there have been strong arguments for integrating cost-of-illness (COI) data into the NHEAs (Thorpe, 1999; Rosen and Cutler, 2007), linking microdata from national expenditure surveys to the macrodata in these accounts.

A similar deficiency limits the value of the medical care information in the NIPAs. Output estimates exist for the medical care sector and subsectors (for example, the ambulatory care subsector), but nowhere in the NIPAs is information presented on the products that the medical care sectors produce. Adding COI estimates to the NHEAs and the NIPAs can provide this critical information. Thus, a central issue in expanding either account of medical care is adding the disaggregated microdata needed to estimate treatment of disease costs.

As discussed in Chapter 2, linking health care spending to the treatment of specific diseases is useful in several respects. It provides a framework for understanding changes in the cost and quantity of health care, and it makes it possible to distinguish the effects of increasing prices for health care from the effects of increasing provision of services. Disease-based accounts also provide useful indicators of the economic burden individual diseases place on society; they can also be used to help identify how health resources are currently allocated, including across different population subgroups (informing questions of



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