2011 VIReC Database and Me

Cyber Seminar Series

RESEARCHERS’ GUIDE TO VA DATA
2011 VIReC Database and Methods Cyber Semina

VIReC Database and Methods Cyber Semina

Applying Comorbidity Meas

Using VA and Medicare da

January 3, 2011

Presented by:

Elizabeth Tarlov, RN, PhD

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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 when using administrativ

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, eg . .,

– Sessions 2 & 3

Assess g ea t ca e Use ing VA Healthcare Use

– Session 4 Measuring

Outpatient Pharmacy use in VA

– Sessions 7 & 8

Measuring Health Services Use in VA and M

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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 

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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 questio

requiring information on comorb

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 administrative data to capture comorbidities.

– 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 Como

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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Typ g es of Diagnosis Code

ICD-9-CM Diagnosis Codes1

– International Classification of Diseases International Classification of Diseases,

Ninth Revision, Clinical Modification

– Admitting code - p g atient’s initial diagnosis 

time of admission 

– Primary code - conditions chiefly responsi

th e vi i s t

– Secondary codes - conditions affecting se

provided

1 http://www.cdc.gov/nchs/icd.htm

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p g
Types of Procedure Cod

ICD-9-CM procedure codes

– Used for inpatient services in VA and 

institutional inpatient Medicare claims

CPT® procedure codes1

– Cu e ocedu a e o ogy urrent Procedural Terminology– 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 Primary Secondary ICD-9 

Diagnosis 

Code

y

Diagnosis 

Code

y

Diagnosis 

Codes

Procedure 

Codes

Inpatient X X X Main

Inpatient Bedsection XXXInpatient Procedure X XInpatient X X SurgeryOutpatient VisitOutpatient Event XX 

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Medicare Diagnosis and Proc

Co

des

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 

– When diagnosis information is 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 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 

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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 

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

rehab? expenditures?)

– Data availability - inpatient, outpatient, 

• (e.g., See Klabunde 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 criteria1

• 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, 

 diagnostic imaging, and other a

test events; DME/prosthetics; telephone encounters

– VA – st d op co es

– Medicare – DME File, Physician Specialty c

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

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Measurement Considerations

Identifying Non

-clinician

-ass

Diagnoses

Exampl f li i d d les o

f VA Cli

nic Stop co

des use

d 

identify claims for exclusion

–

X

-ray 105

– Laboratory 108– Telephone 103, 147, 178 (and others)

Examples of Medicare Physician Specicodes used to identify claims for exclu

– Diagnostic radiology Diagnostic 

radiolog

y 30

– Mammography screening center 45– Diagnostic Lab 72

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Important Measuremen

C id ti 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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Sp g ecial Challenges

Measuring functional statusMeasuring severity of diseaseUndiagnosed conditions

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Important Measurement Iss

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 Comorbid

Measures

Using Administrative Dat

ACG HCC/DCGCharlson

– Deyo-Charlson

FunctionaComorbid

– Romano adaptation

Elixhauser

RxRiskOthers

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Charlson Comorbidit

y Ind

Developed to predict mortality19 chronic conditionsEach has a weightScore = sum of weightsExtended/adapted by Deyo, Roma

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HCC / DCG Method

Developed to predict costs15,000 ICD-9 diag p nosis codes pu185 buckets of homogeneous conditionsCondition categories arranged hierarchicallyBuckets grouped with others thalik t Di ti C t G like costs, Diagnostic Cost Group

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Pharmacy Data

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

– I l d 45 h i di t i Includes 45 chronic disease categories

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 Dat

Measure 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
Imp p ortance 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 only one system for dually eligible health care users. Me

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y y yg

2006;44(8):768-773
Imp p ortance of Complete D

Byrne, 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 dlead to RRSs that capture one-third of the toburden. The ratio of RRS when calculated using MedVA separately was approximately 2.4. 

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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 Used VA and/or Medicare Data to AComorbiditiesWhere to Go for More Help 

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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:

I t f biditi Impact of comorbidities on canscreening

Walter, et al. (2009)

Background: Older adults who are unlikely 

years or have significant comorbidities thapreclude treatment are unlikely to benefit colorectal cancer screening.

Objective: Determine whether colorectal ca

screeni i t t d t h lth ld t

ing is targeted to healthy older pat

is avoided in older patients with severe co

Note: Comorbidity as a predictor variableSample: VA patients >70 years old

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Case Study: Impact of comorbidities on cance

screening

Walter, et al. (2009)

Comorbidity data

– VA and Medicare– Inpatient and outpatient

Comorbidity measurement

– Measurement period: 12 months– Anchor: Start of outcome observation period – D 1 d t ti f th Ch l C bidit 

eyo1 adaptation of the Charlson Comorbidity 

• Developed to predict mortality• 19 chronic diseases, weig g hted for strength of a

with mortality

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Case Study: Impact of comorbidities on canc

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 Cherkin DC , Ciol MA Adapting a clinical comorbidity index 

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 cance

screening

Walter, et al. (2009)

Results

– Charlson-Deyo Score

• 0 (best health) 36%• 1-3 (average health) 52%

• >4 (worst health) 12%

– Adjusted Cumulative Incidence of Screening

• 0 47.1 (47.0-47.2)• 1-3 45 9 (45 8 45.9 (45.8-46 0) .• >4 40.5 (40.3-40.7)

Limitation

– Comorbidity index cannot account for all factors th

impact likelihood of screening, e.g., functional stat

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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 AComorbiditiesWhere to Go for More Help 

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VIReC Help

VIReC Web site

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

– Information on VA data sources and how 

access data

– Documentation on some VA datasets, i.e.

Medical SAS Datasets, DSS Clinical NatioExtracts

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VIReC Help (cont’d)

HSRData Listserv

– Join at the VIReC Web site– Discussion among 

 >500 data stewards

managers, and users

– Past messages in archive (on intranet)

VIReC Help Desk

– VIR C t ff ill ti 

VIReC staff will answer your question adirect you to available resources on to

– VIReC@va gov VIReC@va.gov

– (708) 202-2413

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Questions?

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