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Episode-Based Price Indexes: Plans and Progress Ana Aizcorbe Nicole Nestoriak. BEA Advisory Committee Meeting May 4, 2007. There is a growing consensus that price indexes for health care should be based on treatment episodes. .
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Episode-Based Price Indexes:Plans and ProgressAna AizcorbeNicole Nestoriak BEA Advisory Committee Meeting May 4, 2007
There is a growing consensus that price indexes for health care should be based on treatment episodes. • Previous work for specific diseases shows that the issue is numerically important: • Heart attacks (Cutler et. al.) • Cataract (Shapiro/Wilcox) • Depression (Berndt et. al.) • National Academies Panel issued a recommendation for the construction of episode-based indexes.
Preliminary work at BEA confirms the numerical importance of the issue in a dataset that includes a comprehensive list of diseases. Comparison of Price Indexes for Medical Care, 2001-2003 (compound annual growth rates) Provider-Based Disease-Based Source: A. Aizcorbe and N. Nestoriak, “Using Commercially-Defined Episodes of Illness for the Measurement of Health Accounts: A Progress Report,” Paper presented at NBER/CRIW Summer Institute, July 2006
Outline of talk • Provide a progress report on our ongoing work to construct these indexes for a health satellite account. • Provide an outline of next steps • Close talk with two important conceptual issues surrounding episode-based price indexes.
Groupers are one way to identify treatment episodes. Episode groupers are algorithms that sift through claims data and • Look at each claim and decide how the diagnoses fit together (comorbidities) • After a period of time without claims, subsequent care is a new episode (clean days) We consider two commercial groupers (algorithms) • Symmetry Health • Medstat
We apply these groupers to claims data from Pharmetrics to explore implementation issues. • Data contain a large number of claims: • 40 million patients • Over 70 health plans. • Our 10% sample contains • $12 billion paid to providers, • 22 million episodes of care (Symmetry Grouper), and • About 600 different types of episodes. • Price is the amount taken in by provider.
What have we learned so far? • Groupers do not always yield clinically homogeneous episodes • Price indexes can be sensitive to: • how expenditures are allocated over time • the parameters used in the algorithm • features of the underlying claims data • Bottom line: these choices need theoretical justification
1. Assessing homogeneity of episodes using number of modes in distribution of episode lengths • We take the presence of more than one mode as evidence of heterogeneity. • This may not present problems if the distributions are stable.
2. Sensitivity of price indexes to expenditure allocation • Fluctuations in the average episode length accounts for measured differences in price/day vs. price/episode. • We believe these fluctuations are an artifact of the data.
3. Sensitivity of price indexes to choice of grouper Fisher Indexes of price per day • Both the trends and contours differ. • Odd seasonal pattern in the Medstat episodes • Price per day declines with length of episode • Symmetry’s definition for chronic episodes
4. Sensitivity of price indexes to underlying data Fisher Indexes of price per episode ________________________________ Price growth is higher in the Ingenix data... …one can not appeal to “law of large numbers.”
Current thinking • One cannot take literal read of data or episodes. • Key is to find a way to use what is available to create a data set that is: • representative of all US patients, • with clinically homogeneous episodes, and • a sensible way to deal with chronic episodes
Next steps We’ve constructed standard errors for price indexes that we will use to address: Homogeneity issue: Is there a tradeoff between granularity and precision of the price indexes? Sensitivity of price indexes: To what extent are differences in price indexes “statistically significant?” We will devise a plan for extracting a representative sample from the Pharmetrics database. Looking ahead, we would like to construct price indexes for other patients as well (i.e., Medicare and Medicaid).
Issue 1. Reweighting treatment-based indexes to obtain price indexes by disease does not address the substitution issue. • Assume: • no change in the costs of therapy or drug treatment • Treatment-based indexes will show no price change regardless of weights (Berndt). • But, substitution of drugs for therapy reduces the cost of treating depression. • An episode-based index captures this price decline.
Issue 2. Qualifications for episode-based price indexes. • Episode-based price indexes capture declines in cost from the substitution across treatment types, provided the disease is defined correctly. • These indexes implicitly assume that quality (the impact on health from treatment) is constant. • To the extent that quality is increasing, disease-based indexes provide an upper bound on quality-adjusted price change.