2012 VIReC Database and Me

Cyber Seminar Series

RESEARCHERS’ GUIDE TO VA DATA
2012 VIReC Database and Methods Cyber Semina

Applying Comorbidity Meas

Using VA and Medicare da

April 2, 2012

Presented by:

Denise Hynes, MPH, PhD, RN

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Session Objectives

At the end of this session the participanAt the end of this session, the participanbe able to:

Name 4 sources of comorbidity informaVA administrative dataIdentify 3 common data elements used measuring comorbiditiesRecognize important measurement issencount d h i d i i t ti 

tered 

w

hen using

a

d

minis

trativ

to assess comorbiditiesAvoid common pitfalls in using MedicaVA data together to assess comorbiditi

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Roadmap

This session will

Focus on use of VA and Medicare data obtain information for comorbidity measurementBuild on previous seminars 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 M– M i O t ti t Ph i VA Measuring Outpatient Pharmacy use in VA

RESEARCHERS’ GUIDE TO VA DATA
Roadmap

This session will not

Discuss theoretical or statistical issrelated to accounting for comorbidiin health researchExamine in detail specific comorbidindices or scales

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Session Outline

OverviewFinding Comorbidity Information in VA M di D t Medicare DataUsing Administrative Data to Assess Comorbidities: Important MeasuremeConsiderationsCase Study: Example of VA Study that VA and/or Medicare Data to Assess ComorbiditiesWhere to Go for More Help 

RESEARCHERS’ GUIDE TO VA DATA
Session Outline

OverviewFinding Comorbidity Information in and M di D t d Medicare DataUsing Administrative Data to AssesComorbidities: Important MeasureConsiderationsCase Study: Example of VA Study tUsed VA and/or Medicare Data to AComorbiditiesWhere to Go for More Help 

RESEARCHERS’ GUIDE TO VA DATA
Comorbidity

A concomitant but unrelatpath l i l di thological or diseaseprocess1

1 American Heritage Medical Dictionary

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Comorbidities

Important component in evalu– Clinical outcomes– Resource use (e.g., costs)– Quality of careMa be concept ali ed/ Ma

y be concept

uali

zed/ 

operationalized as– Pre

dictor

– Covariate/confounder

– Moderator– De

pendent variable

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Examples of research questions r

information on comorbiditie

Comparative effectiveness studies

– Is chemotherapy more effective than radiothe

the treatment of endometrial cancer?

Healthcare disparities

– Do comorbidities explain race/ethnic dispariti

kidney transplants?

Healthcare quality

– Are VA patients more likely than those in FFS 

to receive recommended screening tests?

Healthcare costs / Provider product

– Who provides more cost-effective care for dia

endocrinologists, nephrologists or general inte

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Sources of Comorbidity Informa

Administrative Data

Workload (VA) or claims (Medicare, Medicaid) data for diagnosis and proceco

des

Pharmacy data for medications specifidi / diti disease

/condition

Lab data for laboratory results indicatinconditionOther, e.g., program enrollment record

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Audience Poll

Rate your experience with using ad i i t ti d t t t dministrative data to capturecomorbidities.

– Novice– Some experience– Expert

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Session Outline

OverviewFinding Comorbidity Information in and M di D t d Medicare DataUsing Administrative Data to AssesComorbidities: Important MeasureConsiderationsCase Study: Example of VA Study tUsed VA and/or Medicare Data to AComorbidities Where to Go for More Help 

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Administrative Data Sources 

Comorbidity Information

Diagnosis and Procedure Codes

– VA workload data

• Medical SAS Datasets• Fee Basis Files

– Medicare claims

• Institutional Standard Analytic Files• Non-institutional Standard Analytic Files• Institutional Stay Level File (MedPAR)

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Administrative Data Sources for Com

Information (cont Information (cont d) ’

Medications

– Pharmacy data 

• e g oral hypoglycemics insulin indicate dia

e.g., oral hypoglycemics, insulin indicate dia

• VA PBM, DSS• In future, Medicare Part D claims

Laboratory Results

– DSS Laboratory Results NDE

• e.g., elevated glycohemoglobin indicates dia

– Not available in Medicare data

Other

– e.g., condition-focused program enrollmen

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Types of Diagnosis Code

ICD-9-CM Diagnosis Codes

1

– International Classification of Diseases International Classification of Diseases,

Ninth Revision, Clinical Modification

– Admittin

g code -

p g atient’s initial dia

gnosis 

time of admission 

– Primary code - conditions chiefly responsi

th e

vi i s

t

– Secondary codes - conditions affecting se

provided

1 htt

p://www.cdc.

gov/nchs/icd.htm

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p g
Types of Procedure Code

ICD-9-CM procedure codes

– Used for in

patient services in VA and 

institutional inpatient Medicare claims

CPT

® procedure codes

1

– Cu e ocedu a e o ogy urrent Pr

ocedu

r

al Termin

ology

– Used for outpatient services in VA

1 http://www.ama-assn.org/ama/pub/physician-resources/solu

managing-your-practice/coding-billing-insurance/cpt.shtml

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Types of Procedure Codes

(cont d) ’

HCPCS (Healthcare Common Procedure Coding System) Codes

1

– Used in Medicare billing– Level 1: CPT

® codes (services & proce

– Level 2: Used to identify products, sup

and services not included in the CPT c(e.g., ambulance service & durable meequipment) 

1 http://www.cms.hhs.gov/MedHCPCSGeninfo/

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VA Diagnosis and Procedure 

Admitting 

Diagnosis 

Code

Primary 

Diagnosis 

Code

Secondary 

Diagnosis 

Codes

ICD-9 

Procedure 

Codes

Code Code Codes Codes 

Inpatient Main XXXInpatient

Bedsection XXXInpatient 

Procedure XX XInpatient Surgery XX XOutpatient

VisitOutpatient 

Event XX Inpatient Encounters XX 

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VA Fee Basis Diagnosis and Pro

Codes

Discharge 

Diagnosis

Secondary 

Diagnosis

ICD-9 

Procedure

Diagnosis 

Codes

Diagnosis

Codes

Procedure

Codes

Inpatient

X

X

X

Inpatient Ancillary X* Outpatient X 

*Beginning FY2009

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Medicare Diagnosis and Proc

Codes

Admitting 

Diagnosis 

Code

Primary 

Diagnosis 

Code

Secondary 

Diagnosis 

Codes

ICD-9 

Procedu

Codes

MedPAR X XXInpatient X X XSNF

X

X

X

Outpatient XX Hospice X X XHome Health XX Carrier XX DME

X

X

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Pharmacy Data

Potential value in using pharmacy-based measure versus ICD-based measures 

– Wh di i i f ti i t When diagnosis i

n

formation i

s not 

available

– Stable chronic conditions not occasioning 

provider visit (e.g., hypertension, epilepsy)

– Conditions for which the treatment 

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

RESEARCHERS’ GUIDE TO VA DATA
Session Outline

OverviewFinding Comorbidity Information in and M di D t d M

edicare D

a

t

a

Using Administrative Data to AssesComorbidities: Important MeasureConsiderationsCase Study: Example of VA Study tUsed VA and/or Medicare Data to AComorbiditiesWhere to Go for More Help 

RESEARCHERS’ GUIDE TO VA DATA
Important Measurement Conside

Comorbidities vs. comorbidity burden summary risk measure

– Are specific conditions of interest?– Summary measures 

• Provide one number

—the score the score,

simplifying the analysis 

• Allows for parsimony in statistical regressio

mod l e

s

– Influences data that can be used and cond

to be identified

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Important Measurement Conside

What conditions or condition groupcapture

– Depends on 

• Po

pulation

• Objective (e.g., case-mix adjustment)• Outcome (e.g., mortality? post-stroke 

reh b? dit ?) h

ab? expenditures?)

– Data availability - inpatient, outpatient, 

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

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Important Measurement Conside

What conditions to captureExclude ‘rule-out’ diagnoses

– Operational definition: Any diagnosis t

not meet the following criteria

1

• A t l t d/ l i 

Appears

at least once on

a record/

claim 

inpatient care, or

• Appears on at least two records/claims f

outpatient care with visit/claim dates at days apart.

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

comorbidity: a comparison of hospital records and Medicare 

for cancer patients. Med Care 2006; 44: 921-28

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Important Measurement Conside

What conditions to captureExclude ‘rule-out’ diagnosesIdentify clinician-assigned diagnoses

– Avoid lab, g g g, 

 dia

gnostic ima

ging, and other a

test events; DME/prosthetics; telephone encounters

– VA – st d op co

des

– Medicare – DME File, Physician Specialty c

Claim type code BETOS Place of Service Claim type code, BETOS, Place of Service 

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Important Measurement Conside

Identifying Non

-clinician

-assig

Diagnoses

Examples of VA Clinic Stop codes used identif

y claims for exclusion

– X-ray 105– Laboratory 108– Tl h e ep

hone 103 147 178 ( d th ) 103, 147, 178 (and 

others

)

Examples of Medicare Physician Specicodes used to identify claims for exclu

– Diagnostic radiology 30– Mammography screening center 45– Diagnostic Lab 72

RESEARCHERS’ GUIDE TO VA DATA
Important Measuremen

Considerations

What conditions to captureExclude ‘rule-out’ diagnosesId tif li i i Identif

y

cli

nician-assi d igned 

diagnosesM t ti i d Measurement time period 

–

– Active diagnoses

– T l l ti hi b t 

Temporal relationshi

p b

e

tween comor

measurement and outcome measurem

– Anchor

• Date• Event

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Important Measurement Conside

Special Challenges

Measuring functional statusMeasuring severity of diseaseUndiagnosed conditions

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Important Measuremen

Considerations

Comorbidity measurement using administrative (i.e., electronic healrecord

) data is tied to healthcare u

– Without a healthcare encounter, there i

record generated, diagnosis recorded.

– Health services obtained outside the VA 

generate procedure and diagnosis code

available in VA data (except in the case basis care).

– More frequent users of care will have m

opportunities for diagnoses made and r

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Analytic Strategies in Comorb

Measurement Using Administrativ

OrdinalWeightingCategorical

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

Methodology, design, and analytic techniques to address measuremecomorbid disease. J Gerontol A Biol Sci Med Sci. 2007;62(3):281-285

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Commonly Used Comorbidity M

Using Administrative Dat

ACG HCC/DCGCharlson Functiona

– Deyo-Charlson– Romano ada

ptation

ComorbidRxRisk

Elixhauser Others

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Charlson Comorbidity Ind

Developed to predict mortality19 chronic conditionsEach has a wei

ght

Score = sum of weightsE t d d/ d t d b E

x

ten

d

ed/

a

dap

t

ed b

y

Deyo, Roma

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HCC / DCG Method

Developed to predict costs15,000 ICD-9 diagnosis codes pu

– 185 buckets of homogeneous conditions

Homogeneous condition categor(buckets) arranged hierarchicall

– Within single organ system– Patients falling into more than one buck

organ system assigned to one with high

resource use

HCC/DCG risk scores calculated

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Pharmacy Data

Example: VA-based version of RxRisk(Chronic Disease Score)

– I l d 45 h i di t i Inclu

des 45 

c

hronic disease ca

tegories

identified through prescription data 

– See Sloan KL et al Construction and See Sloan KL, et al. Construction and 

characteristics of RxRisk-V: a VAadapted pharmacy-based case-mix i nstrument. M d C e are 2003 41(6) 2003; 41(6):761-74

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Combining VA and CMS Data to 

Comorbidities

Pitfall #1: Not usin

g both data so

Differing incentives to record cominformationDates-of-service issues may impameasurement ti i d t time perio

d

– VA and Medicare inpatient care: exact dia

date usually not captured 

– Medicare: some services billed periodicall

home health

Multi

ple types of codes

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p yp
Importance of Complete D

Incomplete health status information

– Byrne, et al. 20061 

Effect of using information from only one dually eligible health care users

– Objective: Determine whether all diagn

total illness burden of patients who use boand Medicare health care systems can be from examination of data from only one osystems

– Calculated risk scores using VA only, M

only and both VA and Medicare data only, and both VA and Medicare data

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

from onl

y one s

ystem for duall

y eli

gible health care users. Me

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y y yg

2006;44(8):768-773
Importance of Complete D

B

yrne, et al., 2006

On average for a given patient who used boMedicare services, more diagnoses were rec

y, ,

Medicare (~13– 15) than in the VA system (dual users. On average only 2 diagnoses were common the VA and Medicare. Medicare data alone accounted for app 

roxim

of individuals’ total illness burden, and VA dcaptured one-third of the total illness burdeTh ti f RRS h l l t d i M Th

e rati

o

of RRS

s

w

hen calcula

t

ed using M

e

VA separately was approximately 2.4. 

RESEARCHERS’ GUIDE TO VA DATA
Session Outline

OverviewFinding Comorbidity Information in and M di D t d M

edicare D

a

t

a

Using Administrative Data to AssesComorbidities: Important MeasureConsiderationsCase Study: Example of VA Study Used VA and/or Medicare Data to AComorbiditiesWhere to Go for More Help 

RESEARCHERS’ GUIDE TO VA DATA
Case Study

Walter LC, Lindquist K, Nugent S, et aImpact of age and comorbidity on cocancer screening among older veteraIntern Med. 2009;150(7):465-473.

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Case Study:

Impact of comorbidities on c

screening

Wal l( ) ter, et al. (2009)

Background: Older adults who are unlikely 

years or have significant comorbidities tha

preclude treatment are unlikely to benefit 

colorectal cancer screening.

Objective: Determine whether colorectal ca

screeningg y p is targeted to healthy older pat

is avoided in older patients with severe co

Note: Comorbidity as a predictor variable

Sample: VA patients >70 years old

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Case Study:

Impact of comorbidities on ca

screening

l l( )

Comorbidity data

Walter, et al. (2009)

– VA and Medicare

– Inpatient and outpatient

Comorbidity measurement

– Measurement period: 12 months– A h St t f t b ti i d 

Anchor: Start of outcome observation period 

– Deyo1 adaptation of the Charlson Comorbidity 

• Developed to predict mortality• 19 chronic diseases, weighted for strength of a

with mortality

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Case Study:

Impact of comorbidities on ca

screening

Walter, et al. (2009)

Comorbidity measurement (cont’d)

– Categorized scores

, ()

• 0 = No significant comorbidity• 1-3 = Average comorbidity• >4 S bidit 4 = Severe comorbidity

– Additional measure: 

• Homebound (enrollment in VA Home-Base

Care at start of 2001)

1 Deyo RA Cherkin DC Ciol MA Adapting a clinical comorbidity index 1 Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index 

9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-61

2 Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of clas

comorbidity in longitudinal studies: development and validation. J Ch

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1987;40(5):373-383
Case Study:

Impact of comorbidities on ca

screening

Results

Walter, et al. (2009)

– Charlson-Deyo Score

• 0 ( ) best health) 36%

• 1-3 (average health) 52%

• >4 (worst health) 12%

– Adjusted Cumulative Incidence of Scree

• 0 47.1 (47.0-47.2)

• 1-3 45.9 (45.8-46.0)• >4 40.5 (40.3-40.7)

Limitation

– Comorbidity index cannot account for all fact

impact likelihood of screening, e.g., functiona

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p g, g ,
Session Outline

OverviewFinding Comorbidity Information in and M di D t d M

edicare D

a

t

a

Using Administrative Data to AssesComorbidities: Important MeasureConsiderationsCase Study: Example of VA Study tUsed VA and/or Medicare Data to AComorbiditiesWhere to Go for More Help 

RESEARCHERS’ GUIDE TO VA DATA
VIReC Help

VIReC Web site

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

– Inf d dh ormation on VA data sources and how 

access data

– Documentation on some VA datasets, i.e.

Medical SAS Datasets, DSS Clinical Natio

Extracts

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VIReC Help (cont’d)

HSRData Listserv

– Join at the VIReC Web site– Discussion amon

g 

 >650 data stewards

managers, and users

– Past messages in archive (on intranet)

VIReC Help Desk

– VIR C t ff ill ti 

VIR

eC 

s

t

aff 

will answer your question a

direct you to available resources on to

– VIReC@va gov VIReC@va.gov

– (708) 202-2413

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Questions?

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Upcoming Seminar

May 7Assessing Race and Ethnicity

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