what should we measure and when should we measure it belinda gabbe achrf 2012

What should we measure and when

should we measure it? -

An academic’s perspective A/Prof Belinda Gabbe

Department of Epidemiology and Preventive Medicine

School of Public Health and Preventive Medicine

2

Key factors considered in measurement

Why?

– What is the end-game?

What?

– What domains or outcomes are important for the end-game?

– Are there valid and reliable measures for these?

How?

– Budget

– Loss to follow-up and missing data

– Validity

When?

– Relevance to the population studied

What do we usually want to know?

Describe the population

Quantify outcomes

– Short term

– Long term

Identify groups at risk of poor

outcome

Measure change and

recovery

Measure impact of changes to

system of care or scheme

3

Key descriptors of the population

Demographic

– Age and gender

– Level of education

– Work status and occupation

– Socioeconomic status

Pre-injury health and levels of disability

– Comorbidity/obesity

– Quality of life, function, mental health

Characterisation of injury

– Event characteristics

– Severity

– Management

4

Key outcome measures - person

5

Lyons et al. Int J Inj Contr Safety Promot 2010;17:145-149

Health-related quality of life EQ-5D, SF-12, SF-36, HUI, PedsQL, CHQ

Function/disability WHODAS, GOS-E, GOS, FCI, FIM, SMFA

Pain McGill Pain Questionnaire, NRS, VAS

Post-traumatic stress disorder (PTSD) Various screening tools

Return to work and work disability SIP-work scale, Work Limitations Q

Physical activity participation IPAQ, Short IPAQ

Alcohol and drug use AUDIT

Mental health HADS

Other domains Expectation of recovery

Perceived severity

IES

28th February 2011 Presentation title 6

Key claim descriptors and outcomes

Claim management

Group and interventions

Claimant descriptors

Legal representation

Outcomes

Common law

No-fault dispute

Costs and service utilisation

Income payments and return to work

Additional event details

Fault and crash characteristics

7

28th February 2011 Presentation title 8

Linkage is key

Victorian Orthopaedic Trauma Outcomes

Registry (VOTOR)

Sentinel site registry (4 hospitals)

All orthopaedic trauma admissions with length of stay >24 hours

Approximately 3500-4000 admissions per year

34% transport-related

Hospital and post-discharge data

Follow-up at 6 and 12-months post-injury

DEPM prepares cases for linkage

1. DEPM Link ID

2. TAC claim number

3. Patient Identifiers

TAC prepares cases for CRD

1. DEPM Link ID remains

2. TAC claim number removed and

replaced with CRD number

3. Patient Identifiers removed

CRD prepares cases for DEPM


2. TAC claims data added

DEPM prepares cases for

analysis


2. TAC claims data remains

3. VSTR/VOTOR data added

Linkage Process

Linked data questions

1. What is the profile of TAC clients captured by VOTOR? Has the profile changed

over time?

2. What is the difference in injury profile and outcomes for Recovery and

Independence branch TAC clients? What proportion of VOTOR clients are

managed by the Recovery and Independence branches?

3. What TAC and VOTOR factors best predict the patient-reported, common law

claims, legal representation and claim costs?

4. Does the linkage of TAC and VOTOR data provide better prediction of

outcomes than either source alone?

5. Is there agreement between the injury categorisation recorded by TAC and

the ICD-10 based classifications recorded by VOTOR?

6. Is there agreement between self-reported return to work status (VOTOR) and the

measures of return to work recorded in the TAC claims data?

Summary of linked data

TAC VOTOR VOTOR and TAC

Claim division

Injury group

Common law indicator

No-fault dispute indicator

Costs data

Service utilisation

Income replacement

Age

Sex

Comorbid status

Pre-injury disability

Educational level

Occupation

Discharge destination

Injuries sustained

Function, pain, return to work,

health-related quality of life at 6

and 12-months

Age

Sex

Pre-injury employment

Mechanism of injury

Overview of linked cases - VOTOR

• 3,798 VOTOR cases with a date of injury October 2004 to June 2011

• Mean (SD) time since injury was 1.9 (1.1) years, 46% >2 years post-injury

• 67% male, 46% <35 years of age

• 70% no pre-existing conditions

• 88% no pre-injury disability

• 51% motor vehicle, 26% motorcycle-related

• 75% employed prior to injury

Comparison of injury profile – TAC mild ABI (n=927)

Comparison of injury profile – TAC limb fracture

(n=1206)

0 5 10 15 20 25 30 35

Spine, upper and lower extremity

Spine and lower extremity

Spine and upper extremity

Spinal injuries only

Upper and lower extremity

Multiple lower extremity

Isolated lower extremity

Multiple upper extremity

Isolated upper extremity

Soft tissue injury

Head injury

Chest/abdominal injury

% of cases classified as limb fracture by TAC

Predictors of patient-reported outcomes Functional recovery

(12-months)

Physical health

(12-months)

Mental health

(12-months)

Persistent pain

(12-months)

Return to work

(12-months)

Age

Level of education

Comorbid status


Orthopaedic injuries

Claim division

Age

Gender



Level of education


Mechanism of injury


Associated injuries

Age

Gender

Comorbid status



Mechanism of injury

Age

Level of education


Mechanism of injury

Gender

Level of education


Occupation


Claim division

16

Predictors of TAC scheme outcomes

High cost claim

(12-months)

Total claim cost No fault dispute Common law claim

Age

Gender

Comorbid status



Associated injuries

Claim division

Age

Gender

Comorbid status

Level of education




Associated injuries

Claim division

Gender



Gender

Comorbid status



Mechanism of injury


17

Prediction of return to work at 12-months 0.0

00.2

50.5

00.7

51.0

0

Sen

sitiv

ity

0.00 0.25 0.50 0.75 1.001-Specificity

VOTOR TAC

VOTOR & division TAC with VOTOR

Reference

Prediction of high cost claim at 12-months

0.0

00.2

50.5

00.7

51.0

0

Sen

sitiv

ity

0.00 0.25 0.50 0.75 1.001-Specificity

VOTOR TAC

VOTOR & division TAC with VOTOR

Reference

Closing comments

Key factors to measure

– Demographic and pre-injury characteristics

– Characterisation of injury

– Patient-reported and scheme outcomes

Consistent, standardised data collection

Data linkage is powerful

– Increased prediction

– Efficient

Collaboration between scheme experts and academics important

20

This project is funded by the Transport Accident Commission (TAC),

through the Institute for Safety, Compensation and Recovery

Research (ISCRR).

what should we measure and when should we measure it belinda gabbe achrf 2012

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