Challenges measuring healthcare costs 

attributable to an individual chronic condition 

Steve Zeliadt, PhD MPH

Seattle-Denver VA HSR&D Center of Innovation

HERC Health Economics Cyberseminar

October 16, 2013

IIR 07-235/Zhou “Modeling of Health Care Costs (MHCC) of Veterans with Chronic Diseases” 
Today’s goals

After this session you should be able to:

• Describe causality in the context of costs

• Know different approaches that have been used to estimate 

attributable costs in cross sectional administrative data

• Understand challenges with cost-attribution problem

• Be skeptical when you read “condition _____ costs XXX 

billions of dollars”
General Causal Framework

 Exposure Outcome

Smoking

Calcium deficiency

Pregnancy

Condition X

Lung cancer

Fracture

Health care utilization

Medical costs
Complex in reality...

 

Exposure Outcome

Measured

Confounders

Un-Measured

Confounders
Attributable Cost

• Examples from literature:

– “Cancer costs are $125 billion dollars.”

– “Incremental costs of care for individuals with X compared with those without X.”

– “The proportion of total U.S. health care costs attributable to X.”

– “The Institute of Medicine reported that the economic cost of X was $45 billion.”

– “Condition X impacts 55 million adults and costs X annually.”

– “The American Diabetic Association reported that diabetes cost $116 million in excess medical 

expenditures.”

• Imply causality – counterfactual is cost without condition X

– If we cured cancer, would we save $125 billion dollars?
Poll Question

• What is the audience’s interest in measuring costs & 

causality?

– I have written at least one paper on the attributable cost of an 

important condition.

– I’ve never measured attributable costs directly, but I plan to.

– I just want to know so I can write in the first paragraph of my grant 

that the cost of ____ is XXX billion.

– I haven’t thought much about attributable cost before.
Approaches using claims

• Sum all

• Sum primary 


Approaches using accounting data

• Total costs per person

COSTWITH CONDITION X

• Matching 

COSTWITH CONDITION X - COSTWITHOUT CONDITION X = ATTRIBUTABLE COST or NET

• Regression

COST = B0 + B1(Condition) + BX(Confounders) + e
Example in VA

• FY2008

• 20% sample of VA users (n=996,869)

• Total DSS cost - $7 billion (geographic adjustors)

• Selected 31 common conditions

– CMS chronic conditions warehouse

– Other VA priority conditions

– Indicators using AHRQ’s Clinical Classification Software
31 Conditions

• AIDS/HIV

• Alcohol dependence

• Arthritis

• Asthma

• Benign prostatic hyperplasia

• Cancer-Colorectal

• Cancer-Lung

• Cancer-Prostate

• Cancer-Other

• Chronic kidney disease

• Congestive heart failure

• Chronic obstructive pulmonary 

disorder (COPD)

• Dementia

• Depression

• Diabetes without complications

• Diabetes with complications

• Dyspepsia 

• Erectile Dysfunction

• Hepatitis C

• Hypertension

• Ischemic heart disease

• Multiple sclerosis

• Osteoporosis

• Parkinson's disease

• Vascular disorders

• Psychoses

• Spinal cord injury

• Stroke

• Substance abuse

• Tuberculosis

• Traumatic brain injury
Poll – which do you think 

is most expensive for VA?

• AIDS/HIV

• Alcohol dependence

• Arthritis

• Asthma

• Benign prostatic hyperplasia

• Cancer-Colorectal

• Cancer-Lung

• Cancer-Prostate

• Cancer-Other

• Chronic kidney disease

• Congestive heart failure

• Chronic obstructive pulmonary 

disorder (COPD)

• Dementia

• Depression

• Diabetes without complications

• Diabetes with complications

• Dyspepsia 

• Erectile Dysfunction

• Hepatitis C

• Hypertension

• Ischemic heart disease

• Multiple sclerosis

• Osteoporosis

• Parkinson's disease

• Vascular disorders

• Psychoses

• Spinal cord injury

• Stroke

• Substance abuse

• Tuberculosis

• Traumatic brain injury

• Don’t know

• Other
Prevalence 

Condition N Prevalence of 

Condition, % Mean Age, Yrs

Overall 996,869 100% 63

None of the 31 conditions of interest 160,670 16.1% 56

Alcohol dependence 62,228 6.2% 55

Arthritis 162,269 16.3% 66

Benign prostatic hyperplasia 120,343 12.1% 72

Cancer-Prostate 51,786 5.2% 74

Cancer-Other 75,160 7.5% 69

Chronic kidney disease 53,095 5.3% 72

Chronic obstructive pulmonary disorder (COPD) 113,662 11.4% 68

Depression 171,724 17.2% 58

Diabetes without complications 256,341 25.7% 67

Diabetes with complications 72,530 7.3% 66

Dyspepsia 223,345 22.4% 65

Hypertension 546,777 54.8% 68

Ischemic heart disease 189,942 19.1% 72

Psychoses 178,660 17.9% 57

Spinal cord injury 1,923 0.2% 59
Average Total Cost

Condition FY2008 $ 

Overall 7,039

None of the 31 conditions of interest 1,800

Alcohol dependence 16,551

Arthritis 10,190

Benign prostatic hyperplasia 9,569

Cancer-Prostate 9,376

Cancer-Other 16,249

Chronic kidney disease 23,762

Chronic obstructive pulmonary disorder (COPD) 14,961

Depression 13,587

Diabetes without complications 9,927

Diabetes with complications 15,740

Dyspepsia 11,902

Hypertension 8,573

Ischemic heart disease 10,597

Psychoses 13,726

Spinal cord injury 59,606
Attributable Cost

Condition Total Matching Regression1 RegressionM 

Overall 7,039 - - 

None of the 31 conditions of interest 1,800 - - 

Alcohol dependence 16,551 10,045 9,437 4,318 

Arthritis 10,190 3,719 3,187 1,789 

Benign prostatic hyperplasia 9,569 3,145 3,073 1,458 

Cancer-Prostate 9,376 2,738 1,980 1,532 

Cancer-Other 16,249 10,015 9,537 6,954 

Chronic kidney disease 23,762 17,929 16,491 11,311 

Chronic obstructive pulmonary 

disorder (COPD) 14,961 9,008 8,240 4,331 

Depression 13,587 8,034 7,147 2,516 

Diabetes without complications 9,927 3,851 3,394 1,071 

Diabetes with complications 15,740 9,171 8,048 4,028 

Dyspepsia 11,902 6,273 5,921 3,245 

Hypertension 8,573 3,507 3,487 657 

Ischemic heart disease 10,597 4,625 4,720 1,292 

Psychoses 13,726 8,254 6,915 3,342 

Spinal cord injury 59,606 52,515 46,488 44,725 
Expenditures

Condition Matching Regression1 RegressionM 

Overall $7.0 Billion

None of the 31 conditions of 

interest - - - 

Alcohol dependence 625.1 million 587.2 268.7

Arthritis 603.5 517.2 290.2

Cancer-Prostate 141.8 102.5 79.3

Cancer-Other 752.7 716.8 522.7

Chronic kidney disease 952.0 875.6 600.5

Chronic obstructive pulmonary 

disorder (COPD) 1,023.8 936.6 492.3

Depression 1,379.6 1,227.4 432.0

Diabetes without complications 987.2 870.0 274.5

Diabetes with complications 665.2 583.8 292.1

Hypertension 1,917.7 1,906.8 359.4

Ischemic heart disease 878.4 896.5 245.4

Psychoses 1,474.6 1,235.5 597.0

Spinal cord injury 101.0 89.4 86.0

... 
Expenditures

Condition Matching Regression1 RegressionM 

Overall $7.0 Billion

None of the 31 conditions of 

interest - - - 

Alcohol dependence 625.1 million 587.2 268.7

Arthritis 603.5 517.2 290.2

Cancer-Prostate 141.8 102.5 79.3

Cancer-Other 752.7 716.8 522.7

Chronic kidney disease 952.0 875.6 600.5

Chronic obstructive pulmonary 

disorder (COPD) 1,023.8 936.6 492.3

Depression 1,379.6 1,227.4 432.0

Diabetes without complications 987.2 870.0 274.5

Diabetes with complications 665.2 583.8 292.1

Hypertension 1,917.7 1,906.8 359.4

Ischemic heart disease 878.4 896.5 245.4

Psychoses 1,474.6 1,235.5 597.0

Spinal cord injury 101.0 89.4 86.0

... 

Total 16.9 Billion 15.7 Billion 7.4 Billion 
Interpretation

• Non-sense estimates are common

– MEPS example “pain” = $300 billion of 1.3 trillion total cost?

– Cost NLBI conditions was nearly $300 billion...

• Is it appropriate to conclude CKD is most expensive condition 

for VHA?

• Correlated conditions “absorb” potential attributable cost

– IHD / hypertension

– Causal pathway/clinical mechanism is not specified a priori

– Heart disease → Depression or Depression → Heart disease
Possible Improvements – 

Advancing Science of Costing

• Specify decision goal the cost model is to inform

• Parse in finer diagnostic categories

– Clinical Classification Software 14,000 ICD9 codes → 285 mutually 

exclusive diagnosis groups

• Game theory/Shapely values

– Starts with amount spent (top-down)

• Outline a priori clinical pathways/causal associations
Longitudinal/Time series analyses

• Longitudinal data strong foundation for causal associations

• Disease cost trajectory

Cost

Time 
Onset of Condition

Cost

Time 

Date of diagnosis of condition
Counterfactual 

• If the subject had continued on their natural trajectory and 

not developed the condition of interest

• Historical data are increasingly available

– Disease registries/onset information

– Disease phase 

– Focus is on prediction model building vs hypothesis testing model
Prostate cancer treatment example
Healthy screener bias
Discussion

• Use caution interpreting cross-sectional administrative 

estimates of attributable or “causal” costs

• OK statements: 

– “Expenditures among subjects with X condition were XXX” 

– “Expenditures among subjects with X condition were XXX above 

expected costs without X condition.” (included the counterfactual)

• Questions & ideas from the audience – How are you trying to 

estimate causal/attributable costs?