Accounting for Health and Health Care Approaches to Measuring the Sources and Costs of Their Improvement (2010) / Chapter Skim
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3 Allocating Medical Expenditures: A Treatment-of-Disease Organizing Framework
3 Allocating Medical Expenditures: A Treatment-of-Disease Organizing Framework
Pages 71-94
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From page 71... ...
or a broader version designed to track population health status and its determinants -- requires defining useful expenditure categories and then devising a method for allocating economy-wide spending on medical care into those categories. In addition, units of output that are meaningful from a consumer standpoint must be identified in such a way that price and quantity measures can be attached.1 In Chapter 2, we described the two existing accounts for medical care -- (1)
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From page 72... ...
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.
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From page 73... ...
However, if such estimates are made by population subgroups, we might envision needing a price index for each, e.g., females with heart disease, Also, it is difficult to say whether there would be significant gains from adding demographic breakouts within the existing disease grouping detail -- we do not know (very well) the variation in health status gains from various treatments across groups.
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From page 74... ...
. Beyond the United States -- specifically in Australia, Canada, France, Germany, Japan, the Netherlands, Spain, Sweden, and the United Kingdom -- researchers have developed COI estimates to account for national health expen ditures; some have done this within national health accounting frameworks, in anticipation of further development of disease-based satellite accounts (Heijink, Koopmanschap, and Polder, 2006)
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From page 75... ...
If, for example, the costs of heart attacks are attributed to diabetes in one study, hypertension in another, and preexisting coronary heart disease in yet another, the total cost of all diseases will be overestimated. Indeed, a systematic review of COI studies by Bloom et al.
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From page 76... ...
Costs are usually estimated using a top-down approach in which total costs for the health care sector are used as the starting point and some fraction of the sector's costs are attributed to each of the diseases of interest. By constraining to national expenditure totals, general COI studies are considered more method ologically sound (Koopmanschap, 1998)
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From page 77... ...
This three-way matrix would support multiple data tables: total expenditures by disease, payers by disease, and services purchased by disease, among others. The goal for the accounts would be to allocate total personal health care expenditures to a mutually exclusive, exhaustive set of disease categories.
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From page 78... ...
and COI studies together within a common framework for categorically allocating medical expenditures. The remainder of this chapter focuses on the steps needed to achieve this goal.
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From page 79... ...
At the same time, as noted above, figures derived from the microdata must add up to national expenditure totals. Therefore, a central challenge for disease-based national health accounts is identifying individual-level data of sufficiently broad scope that can be linked across surveys and to NHEAs.
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From page 80... ...
There is no single right answer, but inclusion of nonpersonal health care does have one potential drawback when extended to health accounting: the method typically involves estimating the costs for a disease, not for persons with the disease. This implies that total costs for a disease can be translated to costs per capita but not so easily to costs per prevalent case of a disease.
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From page 81... ...
While this may help tailor the classification system to users' needs, it makes standardization efforts difficult. The validity of disease classifications can be optimized, in part, by grouping diagnoses into homogenous, mutually exclusive, exhaustive categories.
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From page 82... ...
The models varied markedly in the data fields used to define patient risk categories and their output. For example, risk-adjustment tools may or may not include age, sex, and secondary diagnoses.
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From page 83... ...
Once a common disease classification system is chosen, the next step will be to implement it in order to generate data that are useful for medical care and health accounting purposes. Recommendation 3.2: Using a population subsample for which good data exist, a pilot study should be undertaken by the Bureau of Economic Analysis using a proposed classification system with the goal of identifying adaptations needed for a national health account.
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From page 84... ...
3.6. ALTERNATIVE APPROACHES TO ALLOCATING MEDICAL CARE EXPENDITURES There are three distinct conceptual approaches to attributing costs to illnesses using medical claims data.
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From page 85... ...
is the grouping of patients' clinical conditions into discrete, clinically homogenous disease categories with similar expected resource consumption. Commercial episode groupers differ in their input data (e.g., Current Procedural Terminology, ICD-9-CM, Healthcare Common Procedure Coding System, National Drug Code, hospital revenue codes)
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From page 86... ...
This approach is designed to produce more valid estimates for patients with multiple chronic conditions, as it better captures expenditures for comorbidities and complications. That said, the regression specification typically assumes that comorbidities have an independent effect on spending with few interaction terms included in the models.
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From page 87... ...
First, the different grou pers vary markedly in the data inputs required to define patient risk categories and then, in turn, outputs. For example, the groupers may or may not include age, gender, or secondary diagnoses.
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From page 88... ...
The Cave Grouper groups over 14,000 unique ICD-9 diagnosis codes into 526 meaningful medical conditions. The CCGroup Efficiency Care Module takes the output from the Cave Grouper and develops specialty-specific physician effi ciency scores that compare individual physician efficiency (or physician group efficiency)
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From page 89... ...
DCGs begin with 118 condi tion categories determined by age, gender, and diagnosis codes; RxGroups add pharmacy data as an input. Both models create coherent clinical groupings and employ hierarchies and interactions to create a summary measure, the "relative risk score," which can be used to predict health care utilization.
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From page 90... ...
Symmetry software from Ingenix was used to link medical expenditures to disease categories. In order to reconcile the three approaches to common disease categories, the researchers first mapped ICD-9 codes into CCS categories.
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From page 91... ...
This may have something to do with the way risk factors for diseases are commonly coded. For example, physicians may be more likely to code coronary heart disease than they are diabetes, hyperten sion, or hyperlipidemia.
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From page 92... ...
Recommendation 3.3: The Bureau of Economic Analysis (working with aca demic researchers and with the Bureau of Labor Statistics) should continue to investigate the impact of different expenditure allocation approaches -- particularly the episode- and person-based methods -- on price index con
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From page 93... ...
, should collaborate on work to move incrementally toward the goal of creating disease-based expenditure accounts by attempting a "proof of concept" prototype. Using a subgroup of the population with good data coverage, the prototype would attempt to demonstrate that dollars spent in the economy on medical care can be allocated into disease categories in a fashion that yields meaningful information.
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