RESEARCHERS’ GUIDE TO VA DATA

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2013 VIReC Database and Methods Cyber Seminar Series 

Applying Comorbidity Measures 

Using VA and CMS 

(Medicare/Medicaid) Data

June 3, 2013

 Presented by:

Denise Hynes, MPH, PhD, RN

With assistance from Thomas Weichle, MS
RESEARCHERS’ GUIDE TO VA DATA

Session Objectives

At the end of this session, the participant will 

be able to:

Name 4 sources of comorbidity information in 

VA and CMS data

Identify 3 common data elements used in 

measuring comorbidities

Recognize important measurement issues 

encountered when using administrative data 

to assess comorbidities

Avoid common pitfalls in using VA and CMS 

(Medicare/Medicaid) data together to assess 

comorbidities

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RESEARCHERS’ GUIDE TO VA DATA

Roadmap

3 

This session will:

Focus on use of VA and CMS data to obtain 

information for comorbidity measurement

Build on previous seminars, e.g.,

– Assessing VA Health Care Use: Inpatient

– Assessing VA Health Care Use: Outpatient 

– Measuring Health Services Use in VA and Medicare

– Measuring Outpatient Pharmacy Use in VA
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Roadmap

This session will not:

Discuss theoretical or statistical issues 

related to accounting for comorbidities 

in health research

Examine in detail specific comorbidity 

indices or scales
RESEARCHERS’ GUIDE TO VA DATA

Session Outline

Overview 

Finding Comorbidity Information in VA 

and CMS Data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities

Where to Go for More Help 

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RESEARCHERS’ GUIDE TO VA DATA

Session Outline

6 

Overview

Finding Comorbidity Information in VA 

and CMS Data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities

Where to Go for More Help 

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RESEARCHERS’ GUIDE TO VA DATA

Comorbidity

A concomitant but unrelated 

pathological or disease process1 

Several variations on this concept have 

emerged2 

1 American Heritage Medical Dictionary

2 Valderas, JM, Starfield, B, Sibbald, B, et al., Defining Comorbidity: 

Implications for Understanding Health and Health Services

Ann Fam Med. 2009 July; 7(4): 357–363. 

. 7 
RESEARCHERS’ GUIDE TO VA DATA

Comorbidities

8 

Important component in evaluating

– Clinical outcomes

– Resource use (e.g., costs)

– Quality of care

May be conceptualized/ operationalized as

– Predictor

– Covariate/confounder

– Moderator

– Dependent variable
RESEARCHERS’ GUIDE TO VA DATA

Examples of research questions requiring 

information on comorbidities

Comparative effectiveness studies

– Is chemotherapy more effective than radiotherapy in 

the treatment of endometrial cancer?

Healthcare disparities

– Do comorbidities explain race/ethnic disparities in 

kidney transplants?

Healthcare quality

– Are VA patients more likely than those in FFS Medicare 

to receive recommended screening tests?

Healthcare costs / Provider productivity

– Who provides more cost-effective care for diabetes –

endocrinologists, nephrologists or general internists?

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RESEARCHERS’ GUIDE TO VA DATA

Sources of Comorbidity Information in 

Administrative Data

Workload (VA) or claims (Medicare, 

Medicaid) data for diagnosis and procedure 

codes

Pharmacy data for medications specific to a 

disease/condition

Lab data for laboratory results indicating a 

condition

Other, e.g., program enrollment records

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RESEARCHERS’ GUIDE TO VA DATA

Audience Poll

11

Rate your experience with using 

administrative data to capture 

comorbidities.

– Novice

– Some experience

– Expert 

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RESEARCHERS’ GUIDE TO VA DATA

Session Outline

12

Overview 

Finding Comorbidity Information in VA 

and CMS Data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities 

Where to Go for More Help 

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RESEARCHERS’ GUIDE TO VA DATA

Administrative Data Sources for 

Comorbidity Information

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Diagnosis and Procedure Codes

– VA workload data

• Medical SAS Datasets

• Fee Basis Files

– Medicare claims

• Institutional Standard Analytic Files (SAFs)

• Non-Institutional Standard Analytic Files (SAFs)

• Institutional Stay Level File (MedPAR)

– Medicaid claims

• Medicaid Analytic Extract (MAX) Files
RESEARCHERS’ GUIDE TO VA DATA

Administrative Data Sources for 

Comorbidity Information (cont’d)

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Medications

– Pharmacy data 

• e.g., oral hypoglycemics, insulin indicate diabetes

• VA PBM, DSS

• Medicare Part D claims

• Medicaid Prescription Drug claims

Laboratory Results

– DSS Laboratory Results NDE

• e.g., elevated glycohemoglobin indicates diabetes

– Not available in Medicare data

Other

– e.g., condition-focused program enrollment
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Types of Diagnosis Codes

ICD-9-CM Diagnosis Codes1

– International Classification of Diseases, 

Ninth Revision, Clinical Modification

– Admitting code - patient’s initial diagnosis at the 

time of admission 

– Primary/principal codes - conditions chiefly 

responsible for the visit/admission

– Secondary codes - conditions affecting services 

provided 

– Line item code - diagnosis supporting 

procedure/service on the non-institutional claim

1 http://www.cdc.gov/nchs/icd.htm 
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Types of Procedure Codes

ICD-9-CM procedure codes

– Used for inpatient services in VA, 

institutional inpatient Medicare claims, 

and inpatient and other services in 

Medicaid claims

CPT® procedure codes1 

– Current Procedural Terminology

– Used for outpatient services in VA 

– Used for inpatient and other services in 

Medicaid claims

1 http://www.ama-assn.org/ama/pub/physician-resources/solutionsmanaging-your-practice/coding-billing-insurance/cpt.shtml 
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Types of Procedure Codes 

(cont’d) 

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HCPCS (Healthcare Common Procedure 

Coding System) Codes1 

– Used in Medicare/Medicaid billing

– Level 1: CPT® codes (services & procedures)

– Level 2: Used to identify products, supplies, and 

services not included in the CPT codes (e.g., 

ambulance service & durable medical equipment) 

http://www.cms.hhs.gov/MedHCPCSGeninfo/ 
RESEARCHERS’ GUIDE TO VA DATA

VA Diagnosis and Procedure Codes

Principal 

Admitting 

Diagnosis 

Code

Primary 

Diagnosis 

Code

Secondary 

Diagnosis 

Codes

ICD-9 

Procedure 

Codes

CPT 

Procedure 

Codes

Inpatient 

Main X X X 

Inpatient 

Bedsection X X X 

Inpatient 

Procedure X X X 

Inpatient 

Surgery X X X 

Outpatient 

Visit 

Outpatient 

Event X X X 

Inpatient 

Encounters X X X 
RESEARCHERS’ GUIDE TO VA DATA

VA Fee Basis Diagnosis and Procedure 

Codes 

 

Discharge 

Diagnosis 

Codes

Secondary 

Diagnosis 

Codes

ICD-9 

Procedure

Codes

CPT 

Procedure 

Codes

Inpatient X X X 

Inpatient 

Ancillary X* X 

Outpatient X X 

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*Beginning FY2009
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Medicare Diagnosis and Procedure Codes

Admitting 

Diagnosis 

Code

Primary 

Diagnosis 

Code

Secondary 

Diagnosis 

Codes

ICD-9 

Procedure 

Codes

HCPCS 

Procedure 

Codes

MedPAR X X X 

Inpatient X X X X 

SNF X X X X 

Outpatient X X X 

Hospice X X X X 

Home Health X X X 

Carrier X X X 

DME X X X 
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Medicaid Diagnosis and Procedure Codes

Principal 

Diagnosis 

Code

Secondary 

Diagnosis 

Codes

ICD-9 

Procedure 

Codes

CPT 

Procedure 

Codes

HCPCS 

Procedure 

Codes

Other 

Services 

X X X* X* X*

Inpatient X X X* X* X*

Long Term 

Care 

X X 

*Procedure coding system variable (ICD-9, CPT-4, or HCPCS) accompanies 

the procedure code variables 
RESEARCHERS’ GUIDE TO VA DATA

Pharmacy Data

Potential value in using pharmacybased measure versus ICD-based 

measures 

– When diagnosis information is not 

available

– Stable chronic conditions not occasioning a 

provider visit (e.g., hypertension, 

epilepsy) 

– Conditions for which the treatment 

regimen is set and time-limited (e.g., TB)

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RESEARCHERS’ GUIDE TO VA DATA

Session Outline

23

Overview 

Finding Comorbidity Information in VA 

and CMS Data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities

Where to Go for More Help 

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

Comorbidities vs. comorbidity burden or 

summary risk measure

– Are specific conditions of interest?

– Summary measures 

• Provide one number—the score, 

simplifying the analysis 

• Allows for parsimony in statistical regression 

models

– Influences data that can be used and conditions 

to be identified

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

What conditions or condition groups to 

capture

– Depends on 

• Population

• Objective (e.g., case-mix adjustment)

• Outcome (e.g., mortality? post-stroke 

rehab? expenditures?)

– Data availability - inpatient, outpatient, or both

• (e.g., See Klabunde 2000; Wang, 2000)

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

What conditions to capture

Exclude ‘rule-out’ diagnoses

– Operational definition: Any diagnosis that does 

not meet the following criteria1 

• Appears at least once on a record/claim for 

inpatient care, or

• Appears on at least two records/claims for 

outpatient care with visit/claim dates at least 30 

days apart.

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1 Klabunde CN, Harlan LC, Warren JL. Data sources for measuring 

comorbidity: a comparison of hospital records and Medicare claims 

for cancer patients. Med Care 2006; 44: 921-28
RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

Identify clinician-assigned diagnoses

– Avoid clinical laboratory, diagnostic imaging 

(radiology, x-ray), and other ancillary test/service 

events; DME/prosthetics; telephone encounters

– VA – stop codes

– Medicare – DME File, Physician Specialty codes, 

Claim type code, BETOS, Place of Service codes

What conditions to capture

Exclude ‘rule-out’ diagnoses

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

Identifying Non-Clinician-Assigned 

Diagnoses 

Examples of VA Clinic Stop codes used to 

identify claims for exclusion

– X-ray 105

– Laboratory 108

– Telephone 103, 147, 178 (and others)

Examples of Medicare Provider Specialty 

codes used to identify claims for exclusion

– Diagnostic radiology 30

– Mammography screening center 45

– Clinical laboratory 69

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

What conditions to capture

Exclude ‘rule-out’ diagnoses

Identify clinician-assigned 

diagnoses

Measurement time period – 

– Active diagnoses

– Temporal relationship between comorbidity 

measurement and outcome measurement

– Anchor 

• Date

• Event

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

Special Challenges

Measuring functional status

Measuring severity of disease

Undiagnosed conditions

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RESEARCHERS’ GUIDE TO VA DATA

Important Measurement Considerations

Comorbidity measurement using 

administrative (i.e., electronic health 

record) data is tied to healthcare use

– Without a healthcare encounter, there is no 

record generated, diagnosis recorded.

– Health services obtained outside the VA will 

generate procedure and diagnosis codes not 

available in VA data (except in the case of feebasis care).

– More frequent users of care will have more 

opportunities for diagnoses made and recorded.

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RESEARCHERS’ GUIDE TO VA DATA

Analytic Strategies in Comorbidity 

Measurement Using Administrative Data1

Ordinal

Weighting

Categorical 

1 Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA. 

Methodology, design, and analytic techniques to address measurement of 

comorbid disease. J Gerontol A Biol Sci Med Sci. 2007;62(3):281-285.

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RESEARCHERS’ GUIDE TO VA DATA

Commonly Used Comorbidity Measures

Using Administrative Data 

ACG

Charlson 

– Deyo-Charlson

– Romano adaptation

Elixhauser

HCC/DCG 

Functional 

Comorbidity Index

RxRisk

Others

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RESEARCHERS’ GUIDE TO VA DATA

Charlson Comorbidity Index 

Developed to predict mortality

19 chronic conditions

Each has a weight

Score = sum of weights

Extended/adapted by Deyo, Romano 

independently

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RESEARCHERS’ GUIDE TO VA DATA

HCC / DCG Method

Developed to predict costs

15,000 ICD-9 diagnosis codes put into

– 185 buckets of homogeneous conditions

Homogeneous condition categories 

(buckets) arranged hierarchically

– Within single organ system

– Patients falling into more than one bucket in 

organ system assigned to one with highest 

resource use

HCC/DCG risk scores calculated

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RESEARCHERS’ GUIDE TO VA DATA

Pharmacy Data

Example: VA-based version of 

RxRisk (Chronic Disease Score)

– Includes 45 chronic disease categories 

identified through prescription data 

– See Sloan KL, et al. Construction and 

characteristics of RxRisk-V: a VAadapted pharmacy-based case-mix 

instrument. Med Care 2003; 41(6): 

761-74

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RESEARCHERS’ GUIDE TO VA DATA

Combining VA and CMS Data to Measure 

Comorbidities

Pitfall #1: Not using both data sources

Differing incentives to record complete 

information

Dates-of-service issues may impact 

measurement time period

– VA and Medicare inpatient care: exact diagnosis 

date usually not captured 

– Medicare: some services billed periodically, e.g., 

home health

Multiple types of codes

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Importance of Complete Data

Incomplete health status information

– Byrne, et al. 20061 

Effect of using information from only one system for 

dually eligible health care users

– Objective: Determine whether all diagnoses and 

total illness burden of patients who use both the VA 

and Medicare health care systems can be obtained 

from examination of data from only one of these 

systems

– Calculated risk scores using VA only, Medicare 

only, and both VA and Medicare data

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1 Byrne MM, Kuebeler M, Pietz K, Petersen LA. Effect of using information

from only one system for dually eligible health care users. Med Care. 

2006;44(8):768-773

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RESEARCHERS’ GUIDE TO VA DATA

Importance of Complete Data

On average for a given patient who used both VA and 

Medicare services, more diagnoses were recorded in 

Medicare (~13–15) than in the VA system (~8) for 

dual users. 

On average only 2 diagnoses were common to both 

the VA and Medicare. 

Medicare data alone accounted for approximately 80% 

of individuals’ total illness burden, and VA data alone 

captured one-third of the total illness burden. 

The ratio of RRSs when calculated using Medicare and 

VA separately was approximately 2.4. 

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Byrne, et al., 2006
RESEARCHERS’ GUIDE TO VA DATA

Session Outline

40

Overview 

Finding Comorbidity Information in VA 

and CMS Data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities

Where to Go for More Help 

40
RESEARCHERS’ GUIDE TO VA DATA

Case Study

French DD, Margo CE, Campbell RR. 

Comparison of complication rates in veterans 

receiving cataract surgery through the 

Veterans Health Administration and 

Medicare. Med Care. 2012; 50(7):620-626.

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RESEARCHERS’ GUIDE TO VA DATA

Case Study:

Comparison of Secondary Surgery Rates 

Controlling for Comorbidities

French, et al. (2012)

Background: This study questioned whether 

something intrinsic to the health care delivery 

system can have an effect on the rate of surgical 

complications.

Objective: To compare the rates of secondary 

surgeries after cataract extraction for patients 

having surgery provided through VA or Medicare.

Note: Comorbidity indicators as predictor variables

Sample: Veterans >65 years old who received 

outpatient cataract surgery during CY2007

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RESEARCHERS’ GUIDE TO VA DATA

Case Study:

Comparison of Secondary Surgery Rates 

Controlling for Comorbidities

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French, et al. (2012)

Comorbidity data

– Medicare, VA, and Fee Basis

– Outpatient

Comorbidity measurement

– Measurement period: CY2007

– Clinically relevant ocular comorbidities identified using 

ICD-9 codes

• Pseudoexfoliation of lens capsule

• Glaucoma/ocular hypertension
Case Study:

Comparison of Secondary Surgery Rates 

Controlling for Comorbidities

French, et al. (2012)

Comorbidity measurement (cont’d)

– Medically relevant comorbidities identified using ICD-9 

codes based on a modified Charlson Comorbidity Index1

• Myocardial infarction

• Congestive heart failure

• Cerebrovascular disease

• Chronic respiratory disease

• Diabetes

• Cancer

• Moderate or sever liver disease

• Renal failure

• Prostatic hyperplasia

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RESEARCHERS’ GUIDE TO VA DATA

Case Study:

Comparison of Secondary Surgery Rates 

Controlling for Comorbidities

Results-Comorbidity Indicators 

– Comorbidities were prevalent in both groups. 

Patients in the Medicare cataract surgery group had 

greater proportions of cancer, chronic respiratory 

disease, prostatic hyperplasia, and congestive heart 

failure.

Limitation

– Exclusion criteria were applied from 2005 forward, which 

could miss diagnoses that existed before that time and 

were never updated in the electronic medical record.

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French, et al. (2012)
RESEARCHERS’ GUIDE TO VA DATA

Session Outline

Overview 

Finding Comorbidity Information in VA 

and CMS data

Using Administrative Data to Assess 

Comorbidities: Important Measurement 

Considerations

Case Study: Example of VA Study that 

Used VA and/or Medicare Data to 

Construct Comorbidities

Where to Go for More Help 

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RESEARCHERS’ GUIDE TO VA DATA

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VIReC Help

VIReC Web site

 http://www.virec.research.va.gov

– Information on VA data sources and how to 

access data

– Documentation on most commonly used VA 

datasets, i.e., Medical SAS Datasets, DSS Clinical 

National Data Extracts, CDW, and VA CMS data 

available through VIReC 

– In the near future, a tutorial providing step-bystep guidance on the construction of a 

comorbidity index.
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–

VIReC Help (cont’d)

HSRData Listserv

– Join at the VIReC Web site

– Discussion among >650 data stewards, 

managers, and users

– Past messages in archive (on intranet)

VIReC Help Desk

– VIReC staff will answer your question and/or 

direct you to available resources on topics

– VIReC@va.gov 

(708) 202-2413
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Questions?
RESEARCHERS’ GUIDE TO VA DATA

Upcoming Seminar

Improving Mortality Ascertainment Using 

the VA Vital Status Dataset

July 22, 2013

Elizabeth Tarlov, PhD, RN

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