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EPI235: Epi Methods in HSR

March 31, 2005 L2

Evaluating Health Services using administrative data 1: Introduction to Risk Adjustment (Dr. Schneeweiss)

This lecture gives an overview of the various approaches of adjusting for confounding typical to Health Services Research. The purpose and mechanics of proprietary and non-proprietary risk-adjustment tools for clinical and administrative data, including DRGs, ACGs, and comorbidity indices will be discussed. Students will explore the value of standard tools for risk adjustment in Health Services studies.

Background reading: Iezzoni LI: Risk and outcomes. In: Iezzoni LI (ed.): Risk adjustment for measuring

healthcare outcomes. Health Administration Press, Chicago, 1997. Schneeweiss S, Maclure M: Use of Comorbidity Scores for Control of

Confounding in Studies using Administrative Databases. Int J Epidemiol 2000,29:891-898.

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Risk adjustment or case mix adjustment

= controlling confounding (selection) bias

Patient characteristics

Provider Outcomes

Avedis Donabedian on quality measures:

Structural quality

Process Quality

Outcomes Quality

Why is it that most quality indicators are measures of structural and

process quality?

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Risk adjustment:

You want to put together a list of factors that are independent predictors of

treatment outcome and may be associated with the provider.

Patient level:

Severity of primary diagnosis

Age

Comorbidities

System level:

Surrounding structures that cannot be influenced by provider

Process of care:

Doc/bed ratio

Equipment

Infrastructure within provider organization

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Classic Epi example:

Exposure: 0 = surgeon A; 1= surgeon B

Disease: 0 = alive after 30 days 1 = dead after 30 days

Confounder: 0 = age <80 1 = age 80

C=1 C=0

D=1 D=0 D=1 D=0

E=1 16 80 E=1 4 200

E=0 184 920 E=0 196 9798

ORc0 = ORc1 =

D=1 D=0

E=1 20 280

E=0 380 10718

ORcrude =

C=1 C=0 C=1 C=0

E=1 204 96 300 D=1 200 200 300

E=0 9994 1104 11098 D=0 1000 9998 11098

P(c) in exposed= ORC-D =

P(c) in non-exposed=

(16*920)/(80*184) = 1.0 1.0

2.0

68%

90%

10.0

Remember: A confounder is an independent risk factor that is unbalanced between exposure groups.

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Risk adjustment

O PF SF e

Outcome indicator

=

Provider

+

Patient factors

+ Structural

factors

+ Random

factors

Outcome indicator = + 0provider + 1PF1 + ...+iPFi + i+1SF1+ ...+ pSFp + e

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Table: Weighted index of comorbidity according

to Charlson et al. and Ghali et al.

Charlson weights

Conditions Ghali weights

1* Myocardial infarct 1* 1 Congestive heart failure 4 1 Peripheral vascular disease 2 1 Cerebrovascular disease 1 1 Dementia - 1 Chronic pulmonary disease - 1 Connective tissue disease - 1 Ulcer disease - 1 Mild liver disease - 1 Diabetes - 2 Hemiplegia - 2 Moderate or severe renal disease 3 2 Diabetes with end organ damage - 2 Any tumor - 2 Leukemia - 2 Lymphoma - 3 Moderate or severe liver disease - 6 Metastatic solid tumor - 6 AIDS -

* Charlson: acute and old MI; Ghali: acute MI=1, old MI = 0

The Charlson Index:

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The Charlson Index for claims data:

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The Chronic Disease Score (CDS):

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Comorbid conditions according to Elixhauser et al.

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Standardized risk adjustment tools:

Pro Con

Considerations for choosing a risk adjustment variable or tool

Reliability

Validity

Burden of data collection

Variation

Extent to which it can control confounding

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How to measure the performance of risk adjustment tools?

Overall disease severity is a complex function of

The importance and severity of the principal diagnosis and

The number and severity of comorbid conditions in relation to the

primary diagnosis

Problem: There is no gold standard of a good risk adjustment measure like “true

comorbidity”

Often used proxi performance measure: How well can a tool predict the study

outcome

Binary outcomes:

AUC (area under the ROC curve) or c-statistics (same)

Metric outcomes: R2

Careful: Association is NOT prediction

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CDS revisited:

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DRGs:

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DRG159

DRG160

DRG162

DRG161

DRGs:

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DRGs:

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ACG

Ambulatory Care Groups, Adjusted Clinical Groups

1) Every ICD-9-CM code was assigned into one of 34 ambulatory diagnostic

groups (ADG).

2) Similar ADGs were collapsed into 12 CADGs

3) Based on the constellation of CADGs patients are placed into one of 25

exclusive major ambulatory categories (MACs)

4) Based on age, gender, presence of specific ADGs and number of ADGs

patients within MACs were further partitioned into 51(!) mutually exclusive

ambulatory care groups (ACGs)

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=> Prediction of future ambulatory care is easier than prediction of health outcomes:

Prior care is a very strong predictor of future care all things equal