Strategies for a BEA Satellite Health Care Account Summary of a Workshop (2009) / Chapter Skim
Currently Skimming:
2 Allocating Nominal Expenditures on Medical Care: A Disease-Based Conceptual Approach
2 Allocating Nominal Expenditures on Medical Care: A Disease-Based Conceptual Approach
Pages 12-29
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 12... ...
Expenditure data are needed for multiple purposes -- for health program administration, for the production of price indexes, for productivity analysis of the economy's medical care sector, for national income and product accounting, for disease treatment monitoring, and for making cross-country comparisons of health systems -- and the ideal data set characteristics and organizational framework will be different for each. Currently, the U.S.
|
From page 13... ...
The first task in the accounting exercise is to allocate nominal expenditures to the various array of outputs. Assuming that patients seek medical care to treat specific conditions or diseases, the medical care output should be defined and arrayed to reflect that consumption objective.
|
From page 14... ...
Are they part of heart disease treatment, or should they be categorized elsewhere? Rosen cited cardiovascular disease as perhaps the classic example of comorbidities.
|
From page 15... ...
In addition, because there might be an acute myocardial infarction or an acute myocardial infarction complicated by congestive heart failure, there can be varying degrees of severity within a given disease; ideally this variation would be accounted for in the expenditure allocation. A number of commercial firms create so-called episode groupers.
|
From page 16... ...
In their broad-based health accounting work, Cutler and Rosen have been using regression techniques to assign spending across disease episode treatments at the person level. The dependent variable is cost, or total expenditures on medical care, which is regressed against a set of disease dummy variables.
|
From page 17... ...
Therefore, adding up simple cases -- diabetes, heart attack, etc. -- is not going to work very well; it might be hard to extrapolate from a 50-year-old's noncomplex heart attack to what would happen with a 70-year-old. Fortunately, data exist with which to investigate these issues; however, the more that the analysis is driven to define activity at group levels, the greater the required sample size becomes.
|
From page 18... ...
Their interest was primarily to further understand the drivers of health care spending growth and the prevalence of medical conditions. The project, a nine-month effort supported by the Pharmaceutical Research and Manufacturers of America, benefited from the advice of several experts on measuring health care expenditures including Linda Bilheimer, Mike Chernew, Joel Cohen, Mark Freeland, Rod Hayward, Steve H effler, and Judy Lave -- several of whom attended the workshop.
|
From page 19... ...
Baseline Purified Service Type NHE Shifts Out Shifts In NHE Hospital 488.6 39.6 0.0 449.0 Physician and clinical 337.9 52.7 0.0 285.2 Dental 73.3 0.0 0.0 73.3 Other professional 45.7 1.8 33.7 77.6 Home health 34.3 5.7 13.3 14.9 Nondurable medical products 30.9 0.0 0.0 30.9 Prescription drugs 157.9 0.0 10.1 168.0 Durable medical equipment 20.8 0.0 8.2 29.0 Nursing home 105.7 0.0 21.3 127.0 Other personal care 46.3 0.0 13.2 59.5 Total 1341.4 99.8 99.8 1314.4 SOURCE: Workshop presentation by Charles Roehrig. The second step of the allocation exercise was to calculate the distribution within each functional expenditure category by population group; the designated groups are the civilian noninstitutionalized population, various institutionalized populations, and active-duty military, because that is how the data sources break down, more or less.
|
From page 20... ...
Similarly, about 82 percent of personal health expenditures were attributable to the civilian noninstitutionalized population, in the sense captured in MEPS, and another 14 percent to the nursing home population. Altarum relied heavily on MEPS for data on the civilian noninstitutionalized population.
|
From page 21... ...
Comparing these levels with those from 1996 allowed Altarum to estimate spending growth rates for medical conditions. Pneumonia, chronic obstructive pulmonary disease, lung cancer, stroke, and coronary heart disease were categories showing the slowest expenditure growth rates, all at 4 percent or less.
|
From page 22... ...
The research objective is to reconcile disease categories among the encounter-, episode-, and person-based regression approaches; to simulate costs of diseases using each; and to compare and contrast the findings. For this project, Rosen and Cutler have been using health claims data for the period 2003-2005 from Pharmetrics Inc.
|
From page 23... ...
For example, physicians may be more likely to code coronary heart disease than they are diabetes, hypertension, or hyperlipidemia. In contrast to the encounter-based approach, which relies entirely on physician coding on claims, one nice feature of the person-based approach is that coding can be captured over time, so more information about multiple conditions can be obtained; the approach also allows the claims data to be supplemented with surveys, injecting information from patients that can enrich the picture.
|
From page 24... ...
added that it was exceedingly v aluable for his agency to see how different the results can be and that there is some purpose -- price index construction, national benefit cost analysis, the national income and product accounts, etc. -- for which each of the constructs may be the best. Likewise, Jack Triplett was encouraged by the work to improve data and
|
From page 25... ...
Service provider data are the starting point for the producer price index at the Bureau of Labor Statistics (BLS) , and Jorgenson expressed the view that this type of information is going to have to play a role for the BEA work, particularly on the industry side of the accounts.
|
From page 26... ...
Aizcorbe agreed, remarking that it is important for the agency to think about what it will be doing 10 years from now; it is not obvious yet how to take the data that underlie the price programs at BLS and use them directly for BEA's purposes. Another aspect of the data coordination task involves reconciling the microdata in MEPS with the national health expenditure accounts, because they don't add up to the same national totals.
|
From page 27... ...
The work by the Cutler-Rosen group has relied heavily on microdata from national surveys that sample individuals, such as MEPS, supplemented with the Medicare Current Beneficiary Survey. As noted above, while the survey data are essential to the accounting project -- and very useful for high-prevalence conditions, particularly the cardiovascular disease and cardiovascular disease risk factors -- there are real sample size inadequacies for conditions with lower prevalence.
|
From page 28... ...
Another problem with existing claims data sources, which BEA is struggling to get a handle on, is that they typically track patients only as long as they are covered by a particular plan. So changes in employment could lead to discontinuities in the data.
|
From page 29... ...
, the satellite account methodology may have to rely on inferences based on more common diseases, at least for a while; this would seem better than no quality adjustment at all.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.