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Predicting Pharmacy and Other Health Care Costs

Arlene S. Ash, PhD

Boston University School of Medicine &

DxCG, Inc.

Academy Health Annual Meeting

San Diego, CA

June 6, 2004

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Predicting Drug and Other Costs from Administrative Data• Use various “profiles”

– Rx

– Dx

– Both

• To predict next year’s costs– Total $– Non-pharmacy $– Pharmacy $

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Data

• 1998-1999 “Commercial Claims and Encounters” Medstat MarketScan

• N ~ 1.3 million – Mean age: 33 yrs– Percent female: 51%

• Diagnoses: ICD-9-CM codes

• Pharmacy: NDC codes

• Costs (incl. deductibles, copays, COB)

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DCG Model Structure

• Diagnoses drive prediction (Risk Score, or RS)– ~15000 Diagnoses group – 781 Disease Groups – 184 Condition Categories (CCs)– Hierarchies imposed 184 HCCs

• Model– Predicts from age, sex and (hierarchical) “CC profile”– One person can have 0, 1, 2 or many (H)CCs– Risks from HCCs add to create a summary RS

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Sample DCG/HCC Year-2 Prediction Prediction for Year 2

$805 48 year old male

$3,512 HCC16: Diabetes w neurologic or peripheral circulatory manifestation

$1,903 HCC20: Type I Diabetes

$266 HCC24: Other endocrine/metabolic/nutritional disorders

$455 HCC43: Other musculoskeletal & connective tissue disorders

_____ $6,941 FINAL PREDICTION (RS)

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Pharmacy Model Structure

• 80,000+ NDC codes 155 RxGroups

• Hierarchies imposed– E.g., insulin dominates oral diabetic meds

• Relevant coefficients add to create a risk score for each person

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NDC codes (n ~ 82,000+)

RxGroups (n = 155)

Aggregated Rx Categories (ARCs)(n = 17)

Rx Classification System

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Sample RxGroup Year-2 Prediction

$3,352 79-year old male

$1,332 RxGroup 23: Anticoagulants (warfarin )

$1,314 RxGroup 42: Antianginal agents

$1,538 RxGroup 116: Oral diabetic agents ______ $7,536 FINAL PREDICTION

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Year-1 Dx and Rx Prevalence

• Diagnoses– 74% have at least one valid ICD-9 code– Mean # of HCCs per person: 2.5

• Pharmacy – 66% have at least one prescription– Mean # of RxGroups per person: 2.5

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Year-2 Costs

• Total Cost (incl., inpatient, outpatient and pharmacy) – Mean: $2,053 – CV: 386

• Non-Pharmacy Cost– Mean: $1,601 – CV: 471

• Pharmacy Cost– Mean: $452– CV: 278

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Predictive Power of Models (Validated R2)

PredictorsTotal $ Non-Pharm $ Pharmacy $

Rx 11.6% 7.1% 48.2%

Dx 14.6% 11.6% 22.5%

Rx & Dx 16.8% 12.4% 49.3%

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Validated Predictive Ratios (E/O)

Rx Dx Rx & Dx

Asthma Dx (n=38,000) 0.90 0.98 1.00

Asthma/COPD Rx (84,000) 0.95 0.86 0.95

Depression Dx (49,000) 0.85 1.01 1.01

Antidepressant Rx (90,000) 0.98 0.82 0.99

Diabetes Dx (33,000) 0.84 1.02 1.03

Diabetes Rx (23,000) 1.01 0.90 1.03

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Take Home Lessons• Predicting next year’s cost is easiest for

Rx $, hardest for Non-Rx$

• Both kinds of data predict well– Dx predicts other costs better

– Rx predicts Rx$ much better than Dx

– Both together are extremely accurate

• The high predictabiity of Rx$ from Rx data bodes ill for the viability of the new Medicare drug insurance product