# Presentation: 589352

## 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.  

- 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

**Notes:**
We’ve obtained patient-level data with which to construct price indexes by disease, that would account for reduced cost due to substitution of treatment but have not gone the extra step to try to capture quality change.

Even so, the differences in BLS-type PPIs and an episode-based index are substantial.  

## 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.

## Progress Report 

## 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.

**Notes:**
On worker turnover:
Do severely ill lose their jobs and hence their coverage?  That is, any chance that the severely ill are underrepresented?
Do they keep coverage when they go on disability?  When they retire?  
What is the percentage of completed episodes in the data?  Why can’t all claims be grouped into an episode?  Are the orphans important?
Growing out East and West:
1.  Was that pretty finished by 2001?

## 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

- 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

- Fisher Indexes of price per day

## 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).

## Two Important Conceptual Issues

## 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.