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
1
Predicting Drug and Other Costs
from Administrative Data
• Use various “profiles”
– Rx
– Dx
– Both
• To predict next year’s costs
– Total $
– Non-pharmacy $
– Pharmacy $
2
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)
3
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
5
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)
6
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
7
Rx Classification System
NDC codes
(n ~ 82,000+)
RxGroups
(n = 155)
Aggregated Rx Categories (ARCs)
(n = 17)
8
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
______
$7,536
RxGroup 116: Oral diabetic agents
FINAL PREDICTION
9
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)
Predictors
Total $ 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%
12
Validated Predictive Ratios (E/O)
Rx
Asthma Dx
Dx
Rx & 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
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