083 VOTOR-TAC Linkage Report - Impact of the TAC Recovery Model

VOTOR-TAC Linkage Report

Impact of the TAC Recovery Model

School of Public Health and Preventive Medicine

Monash University

Professor Belinda Gabbe

Ms Pam Simpson 14th July 2015

Research report #: 083-0715-R03

Research Report # Page 2 of 35

Table of Contents

1. About this report .................................................................................................................. 3

2. Research Questions ............................................................................................................ 3

3. Methods ................................................................................................................................ 3

3.1 Inclusion criteria ........................................................................................................... 3

3.2 Linkage process ........................................................................................................... 3

3.3 Linkage variables ......................................................................................................... 5

3.4 Data analysis ................................................................................................................ 5

3.4.1 Outcomes of interest ................................................................................................................. 5

3.4.2 Modelling approach ................................................................................................................... 5

4. Results .................................................................................................................................. 8

4.1 Brief profile of VOTOR TAC Recovery cases ............................................................... 8

4.2 Impact of the implementation of the Recovery model on 6-month outcomes ...............10

4.2.1 Summary of 6-month outcomes in the pre-Recovery and post-Recovery periods ................. 10

4.2.2 Summary of the findings from the segmented regression modelling at 6-months .................. 11

4.2.3 Impact of the Recovery model on functional outcomes at 6-months (GOS-E) ....................... 13

4.2.4 Impact of the Recovery model on return to work outcomes at 6-months ............................... 15

4.2.5 Impact of the Recovery model on reporting moderate to severe pain at 6-months ................ 16

4.2.6 Impact of the Recovery model on EQ-5D outcomes at 6-months ........................................... 17

4.2.7 Impact of the Recovery model on SF-12 outcomes at 6-months ............................................ 23

4.2.8 Impact of the Recovery model on total TAC claim costs at 6-months .................................... 25

4.3 Impact of the implementation of the Recovery model on 12-month outcomes .............26

4.3.1 Summary of 12-month outcomes in the pre-Recovery and post-Recovery periods ............... 26

4.3.2 Summary of the findings from the segmented regression modelling at 12-months ................ 26

4.3.3 Impact of the Recovery model on functional outcomes at 12-months (GOS-E) ..................... 28

4.3.4 Impact of the Recovery model on return to work at 12-months .............................................. 30

4.3.5 Impact of the Recovery model on reporting moderate to severe pain at 12-months .............. 31

4.2.6 Impact of the Recovery model on SF-12 outcomes at 12-months .......................................... 32

4.2.7 Impact of the Recovery model on TAC claim costs at 12-months .......................................... 34

5. Summary of findings ..........................................................................................................35

6. Implications for the TAC .....................................................................................................35


1. About this report

The ISCRR Victorian Orthopaedic Trauma Outcomes Registry (VOTOR) project plan includes

deliverables related to linkage of VOTOR and TAC claims data at regular intervals. The

VOTOR-TAC Analysis Plan 2015/16 was developed to guide the analysis and reporting from

linked VOTOR, Victorian State Trauma Registry (VSTR) and TAC claims data.

2. Research Questions

The research question addressed in this report was:

What has been the impact of the TAC2015 Recovery model on patient-reported and TAC

scheme outcomes for VOTOR and Victorian State Trauma Registry (VSTR) Recovery clients?

Through discussion with key TAC personnel, it was identified that the initiatives introduced as

part of the Recovery model were not expected to impact on client outcomes. A focus of the

Recovery model was the capacity to segment clients into streams of claim management based

on the complexity of their needs or circumstances, with the segmentation largely focused on the

risk of lodging a common law claim. Recovery Branch processes changed considerably after

the implementation of the model, making it difficult to evaluate as a single intervention. Most

importantly, the introduction of mental health screening and other interventions were not

introduced during the evaluation timeframe, limiting the capacity to impact on client outcomes.

However, this report was completed to establish a baseline to which future evaluations could be

compared.3. Methods

3.1 Inclusion criteria

Cases registered by VOTOR meeting the following criteria were extracted for linkage:

i. Definitive management at The Alfred, RMH, University Hospital Geelong (UHG) or The

Northern Hospital (TNH)

ii. Date of injury from March 2007 to January 2014

iii. Fund source recorded as TAC and a claim number provided by the hospital.

iv. Confirmed as a Recovery Division claimant (or Benefit Delivery for cases before the

Recovery model implementation) client by the TAC flag.

3.2 Linkage process

The Department of Epidemiology and Preventive Medicine (DEPM) first created a unique

linkage ID, accessible to a single individual within the DEPM, for each eligible VOTOR and


VSTR case. The DEPM data administrator obtained the TAC claim number from the electronic

systems of each hospital, and then provided the TAC a file of claims that needed to be matched

which included the DEPM Linkage ID, TAC claim number, and patient identifiers (surname,

given name, date of birth and date of injury). No clinical or outcomes information from

VOTOR/VSTR was provided to TAC. These data were contained in a separate file, held at the

DEPM, and not provided to either of the other two organisations involved in this linkage project.

The TAC then matched the claim numbers and identifiers provided by DEPM to those on the

TAC claims database. For matching files, the TAC produced a file containing the DEPM Linkage

ID, and CRD Linkage ID to the ISCRR investigators. ISCRR did not receive the TAC claim

number or clinical/content information from DEPM. Concurrently, the TAC provided the DEPM

with a list of TAC claim numbers that matched to the TAC database to allow quality assurance

analysis.

For the matching cases, ISCRR appended the relevant TAC claims data to the data file and

removed the CRD Linkage ID. The resultant file was provided to the DEPM data administrator

who linked the data provided by ISCRR to the VOTOR data using the DEPM Linkage ID. The

linked dataset was then provided to the Project Leader (Belinda Gabbe) and DEPM

Biostatistician (Pam Simpson) for cleaning and analysis.


3.3 Linkage variables

A list of the linked variables, and their source, can be provided on request. In addition to the

variables provided by the CRD, the TAC also provided an additional file with selected data items

which were able to be linked using the DEPM ID.

3.4 Data analysis

3.4.1 Outcomes of interest

The outcomes of interest were:

i. Complete functional recovery defined as a GOS-E score of eight (upper good recovery)

at 6-months and 12-months post-injury (VSTR and VOTOR).

ii. Good functional recovery defined as a GOS-E score of seven (lower good recovery) or

eight (upper good recovery) at 6-months and 12-months post-injury.

iii. Return to work defined using the return to work questions collected by VOTOR at 6-

months and 12-months post-injury.

iv. Physical health defined as the physical component summary score of the SF-12 (PCS-

12) at 6-months and 12-months post-injury.

v. Mental health defined as the physical component summary score of the SF-12 (MCS-12)

at 6-months and 12-months post-injury.

vi. Total claim costs at 6-months and 12-months post-injury.

vii. EQ-5D summary score and indicator variables for each of the five EQ-5D items (no

problems vs. problems) at 6 months only.

3.4.2 Modelling approach

The introduction of the Recovery model represents an interrupted time series design, with the

introduction constituting a natural experiment. A simple linear or logistic regression approach

comparing the association between “pre” and “post” intervention phases and the outcome only

allows a comparison of the association before and after, essentially averaging the results over

both time periods. In contrast, segmented regression analysis enables assessment, in

statistical terms, of how much an intervention changed an outcome of interest, immediately and

over time; instantly or with delay; transiently or long-term; and whether factors other than the

intervention could explain the change.

A segmented regression analysis with bi-monthly intervals was used to determine the difference

between outcomes before and after implementation of the Recovery model. To account for


unequal numbers in each segment, the analyses were weighted by the inverse proportion of the

number in each segment. The 6 and 12-month periods before the Recovery phase was

implemented were excluded from the 6 and 12-month analyses, respectively.

All analyses were adjusted for age, gender, road user group, orthopaedic injury group, head

injury, chest or abdominal injury, other non-orthopaedic injury, comorbid status (Charlson

Comorbidity Index), pre-injury work status (for all models except return to work), pre-injury self-

reported disability, geographic remoteness and socioeconomic status. The postcode of

residence was mapped to the Index of Relative Socio-Economic Advantage and Disadvantage

(IRSAD) which summarises information about the economical and social conditions of people

living in a specific area as deciles with one representing the most disadvantaged and 10 the

most advantaged. Similarly, the residential postcode was mapped to the

Accessibility/Remoteness Index of Australia (ARIA) which provides a measure of geographic

remoteness bases on the road distance to service centres in with remoteness classified as one

of five groups; major metropolitan city, inner regional, outer regional, remote or very remote.

The covariates included in the model were those considered a priori to influence the outcomes

and those that have been shown in the past to be associated with each outcome. The

covariates were included in the segmented regression model in the same way as for standard

regression analyses. .

The segmented regression enabled estimation of the following:

i. Baseline trend - the change in outcome per time segment that occurred before the

Recovery model was implemented.

ii. Level of change after the Recovery model was introduced - the change in the outcome

immediately after implementation of the Recovery model.

iii. Trend change after the Recovery model was introduced - the change in outcome per bi-

monthly segment after the Recovery model was introduced compared to the overall

trend before the implementation of the Recovery model.

To provide a visual representation of the findings from the segmented regression models,

graphs showing the Pre-Recovery, at implementation, and Post-Recovery periods were

generated. These graphs show the slope of trend prior to implementation of the Recover

model, the change at the point of implementation, and the slope of trend after introduction of the

Recovery model. For these graphs, the baseline trend coefficient will be consistent with the

Pre-Recovery graph slope. As will the “Level of change” coefficient. However, the “Trend after

the Recovery model was introduced” coefficient takes into account the trend before Recovery

and the difference compared to the trend after Recovery to generate the coefficient. In contrast,


the Post-Recovery slope of the graph only shows the trend after the Recovery model was

introduced. Therefore, the direction of the coefficient for trend after the Recovery model was

introduced may not match the slope of the graph as the coefficient takes into account the Pre-

Recovery and the Post-Recovery trends to form a single coefficient of change.


4. Results

4.1 Brief profile of VOTOR TAC Recovery cases

Registration of VOTOR cases has been continuous at all four hospitals since March 2007 and

therefore, cases from this date were included. There were 7,210 VOTOR cases with a date of

injury from March 2007 to January 2014, flagged as TAC Recovery clients and successfully

linked. Of these, 7,210 cases, 3,205 (44.5%) met major trauma criteria. Table 1 shows the

demographic profile of VOTOR Recovery clients.

Table 1: Demographic profile of VOTOR Recovery clients (n=7,210)

Population descriptor

Age Mean (SD) years 42.4 (18.7)

Gender N (%)

Male

Female

4,820 (66.9)

2,390 (33.1)

Charlson Comorbidity Index weighting N (%)

None

1

>1

5,562 (77.1)

1,381 (19.2)

267 (3.7)

Index of Relative Socioeconomic Advantage and Disadvantage quintilea

N (%)

1 (Most disadvantaged)

2

3

4

5 (Least disadvantaged)

775 (11.0)

742 (10.5)

1,359 (19.3)

2,027 (28.8)

2,414 (30.4)

Geographic remotenessb N (%)

Major cities

Inner regional

Outer regional, remote and very remote

5,423 (77.0)

1,330 (18.9)

293 (4.1)

Level of educationc N (%)

University degree

Advanced diploma/diploma/certificate

Completed high school

Did not complete high school

1,238 (19.6)

1,880 (29.8)

1,042 (16.5)

2,143 (34.0)

Pre-injury work statusd N (%)

Not working

Working

1,756 (26.4)

4,908 (73.6)

Self-reported pre-injury disabilitye N (%)

None

Mild

Moderate/marked/severe

5,721 (86.4)

559 (8.4)

345 (5.2)

a Data missing for n=166 (2.3%); b Data missing for n=164 (2.3%); c Data missing/not yet collected for n=907 (12.6%); d Data missing/not yet collected for n=546 (7.6%); e Data missing/not yet collected for n=585 (8.1%);


Most VOTOR TAC Recovery clients were male, healthy and without disability prior to injury,

living in major cities, and working prior to injury. The proportion of clients living in the most

disadvantaged suburbs was low, despite half of the patient population reporting a high school

level of education or below (Table 1).

The predominant road user types were motor vehicle drivers or passengers, and motorcyclists.

More than 90 per cent were managed at The Alfred and Royal Melbourne Hospital, and isolated

lower extremity injuries and spinal injuries were most common (Table 2). The proportion with

associated chest or abdominal injuries was high, while 40 per cent were discharged to inpatient

rehabilitation (Table 2).

Table 2: Injury and in-hospital outcomes profile of VOTOR Recovery clients (n=7,210)

Population descriptor

Road user group N (%)

Motor vehicle

Motorcycle

Pedestrian

Pedal cyclist

Other

3,537 (49.1)

1,960 (27.2)

1,003 (14.0)

503 (7.0)

207 (2.9)

Hospital of definitive care N (%)

The Alfred

Royal Melbourne Hospital

University Hospital Geelong

The Northern Hospital

3,850 (53.4)

2,970 (41.2)

342 (4.7)

49 (0.7)

Orthopaedic injury group N (%)

Isolated lower extremity injury

Spinal column injuries

Isolated upper extremity injury

Multiple lower extremity injuries

Upper and lower extremity injuries

Spinal column and lower extremity injuries

Spinal column and upper extremity injuries

Spinal column, upper and lower extremity injuries

Multiple upper extremity injuries

Soft tissue injuries only

1,569 (21.8)

1,440 (20.0)

1,019 (14.1)

734 (10.2)

644 (8.9)

580 (8.0)

457 (6.3)

357 (4.9)

251 (3.5)

159 (2.2)

Associated non-orthopaedic injuries N (%) Head injury

N (%) Chest or abdominal injury

N (%) Other non-orthopaedic injur7\y

627 (8.8)

2,749 (38.1)

2,033 (28.2)

Hospital length of stay Median (IQR) days 5.5 (3.0-10.1)

Discharge destination N (%)

Home

Inpatient rehabilitation

Other

4,018 (55.7)

2,883 (40.0)

309 (4.3)


4.2 Impact of the implementation of the Recovery model on 6-month

outcomes

4.2.1 Summary of 6-month outcomes in the pre-Recovery and post-Recovery periods

Table 3 shows the 6-month post-injury patient-reported and cost outcomes of TAC VOTOR

clients before and after the implementation of the Recovery model. The EQ-5D models

included fewer cases as the EQ-5D was not added to the registry follow-up until 2009 (Table 3).

The proportion of VOTOR Recovery cases fully recovered, experiencing a good recovery,

returning to work, reporting moderate to severe persistent pain, mobility limitations, and anxiety

or depression problems were largely similar before and after introduction of the Recovery

model, as were the mean SF-12 summary scores and the EQ-5D summary score. The

proportion reporting problems with self-care and usual activities on the EQ-5D appeared to be

higher following the implementation of the Recovery model. The mean costs were similar

between the two phases (Table 3).

Table 3: Comparison of outcomes pre and post Recovery model at 6-months post-injury

N

Pre-Recovery

Outcome*

N (%)

N

Post-Recovery

Outcome

N (%)

Good recovery 2,687 840 (31.3) 3,229 994 (30.8)

Return to work** 1,964 1,209 (61.6) 2,283 1,377 (60.3)

Complete recovery 2,687 440 (16.4) 3,229 478 (14.8)

Moderate/severe persistent pain 2,132 627 (29.4) 2,605 695 (26.7)

EQ-5D mobility problems 1,171 562 (48.0) 3,205 1,501 (46.8)

EQ-5D self-care problems 1,167 227 (19.5) 3,201 780 (24.4)

EQ-5D usual activity problems 1,164 746 (64.1) 3,203 2,208 (68.9)

EQ-5D pain/discomfort 1,162 823 (70.8) 3,189 2,141 (67.1)

EQ-5D anxiety/depression 1,152 516 (44.8) 3,188 1,457 (45.7)

Mean (SD) Mean (SD)

PCS-12 2,084 38.9 (11.8) 2,484 38.3 (11.9)

MCS-12 2,084 49.0 (12.1) 2,484 50.1 (11.8)

EQ-5D summary score 1,136 0.63 (0.30) 3,168 0.65 (0.29)

Cost ($) 3,042 62,833 (58,570) 3,615 62,547 (57,595)

*Patients with a date of injury in the 6 months prior to the introduction of the Recovery model were excluded from the 6 month summary because their care would be a mixture of the old model and the Recovery model; ** If working prior to injury.


4.2.2 Summary of the findings from the segmented regression modelling at 6-months

The results of the segmented regression modelling to quantify the impact of the introduction of

the Recovery model on VOTOR client outcomes are summarised in Table 4. The baseline

period refers to before the introduction of Recovery model. For each outcome, the log odds of

the baseline trend was not significant, indicating stability in patient-reported outcomes and claim

costs in the period leading up to introduction of the Recovery model.

The “level change” provides an indication of the immediate impact of the Recovery model.

There was a no change in the log odds of most of the patient-reported outcomes at the

implementation phase of the Recovery model (Table 4). Return to work declined, and the log

odd of reporting problems on most of the EQ-5D items increased, at the point of implementation

of the Recovery model (Table 4).

The “trend change” after Recovery provides a measure of change in outcome per bi-monthly

segment after the Recovery model was introduced compared to the bi-monthly trend before the

implementation of the Recovery model. Put simply, this shows the trend in outcomes since the

model was introduced taking into account the Pre-Recovery trends. There was little evidence of

improvement in patient-reported outcomes after implementation of the Recovery model with

only the probability of reporting problems on the EQ-5D anxiety/depression item tracking

downwards (Table 4).


Table 4: Impact of the introduction of the Recovery model on 6-month outcomes – results from the multivariable* segmented regression

Outcome Baseline trend

Log-odds (95% CI)

p-value

Level change after Recovery

Log-odds (95% CI)

p-value Trend change after Recovery

Log-odds (95% CI)

p-value

Good recovery -0.01 (-0.02, 0.01) 0.47 -0.02 (-0.26, 0.22) 0.85 0.01 (-0.01, 0.03) 0.41

Complete recovery 0.001 (-0.02, 0.02) 0.92 0.08 (-0.21, 0.38) 0.59 -0.03 (-0.05, 0.003) 0.08

Return to work** 0.01 (-0.01, 0.03) 0.49 -0.30 (-0.57, -0.04) 0.03 0.01 (-0.02, 0.03) 0.61

Moderate/severe persistent pain -0.004 (-0.02, 0.02) 0.67 -0.02 (-0.30, 0.25) 0.86 -0.003 (-0.03, 0.02) 0.82

EQ-5D mobility limitations -0.04 (-0.10, 0.02) 0.24 0.28 (-0.02, 0.58) 0.07 0.02 (-0.04, 0.08) 0.55

EQ-5D self-care limitations 0.003 (-0.07, 0.07) 0.94 0.40 (0.07, 0.73) 0.02 -0.02 (-0.09, 0.06) 0.67

EQ-5D usual activity limitations 0.01 (-0.05, 0.07) 0.76 0.25 (-0.04, 0.53) 0.09 -0.01 (-0.07, 0.05) 0.68

EQ-5D pain/discomfort -0.05 (-0.11, 0.01) 0.08 0.30 (0.01, 0.60) 0.04 0.02 (-0.04, 0.09) 0.47

EQ-5D anxiety/depression 0.04 (-0.01, 0.10) 0.13 0.02 (-0.25, 0.29) 0.88 -0.05 (-0.11, 0.01) 0.07

Baseline trend

(95% CI)


(95% CI)

Trend change after Recovery

(95% CI)

PCS-12 -0.02 (-0.11, 0.08) 0.75 -0.99 (-2.34, 0.35) 0.15 0.06 (-0.07, 0.18) 0.37

MCS-12 0.04 (-0.06, 0.14) 0.47 0.40 (-0.97, 1.77) 0.57 -0.03 (-0.16, 0.10) 0.64

EQ-5D summary score 0.004 (-0.004, 0.01) 0.34 -0.02 (-0.06, 0.02) 0.31 -0.002 (-0.01, 0.01) 0.69

Baseline trend

%a (95% CI)


%b (95% CI)


%c (95% CI)

Cost ($) -0.02 (-0.6, 0.6) 0.95 -5.0 (-12.4, 3.0) 0.21 0.5 (-0.2, 1.3) 0.17

n.b. Patients with a date of injury 6 months prior to the introduction of the Recovery model were excluded from the analysis because their care would be a mixture of the old model and the Recovery model; *All analyses were adjusted for age, gender, road user group, injury group, head injury, chest or abdominal injury, other injury, comorbid status, pre-injury work status, pre-injury disability, geographic remoteness and socioeconomic status; a Percentage change in 6 month costs per 2 month interval; b Percentage change in 6 month costs immediately after Recovery was introduced; c Percentage change in trend after Recovery compared to percentage change before Recovery was introduced.


4.2.3 Impact of the Recovery model on functional outcomes at 6-months (GOS-E)

Figure 1 and Figure 2 represent the GOS-E good recovery and complete recovery outcomes for

VOTOR Reovery clients, respectively. There was a trend for reporting probability of a good

functional recovery prior to implementation of the Recovery model was stable, and little change

was observed at the point of implementation, and a stable rate of good recovery has been

observed since implementation of the new claims model (Figure 1). There was no evidence of a

change in the probability or reporting a good recovery following introduciton of the Recovery

model (Figure 1 and Table 4).

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Fitted modelBi-monthly proportion whomake good recovery

95% Confidence Interval

Pr(

Go

od

re

cove

ry a

t 6

mth

s)

n.b. graph refers to time post-injury rather than post claim lodgement

Figure 1: Impact of the Recovery model on functional outcomes (GOS-E good recovery)

at 6-months post-injury of VOTOR clients


Figure 2 shows stability in the probability of a complete functional recovery prior to the

introduction of the Recovery model, little change at the point of implementation, and a

downward trend since introduction of the Recovery model (Figure 2). The coefficient describing

the change in trend since the Recovery model was introduced is negative (Table 4), reflecting

the change from a largely flat slope to a downward slope when comparing the pre-Recovery and

post-Recovery periods (Figure 2). However, the downward trend since the implementation of

the Recovery model was not significnat (Table 4). Overall, the implementation of the Recovery

model appears to have had little impact on the functional outcomes of VOTOR Recovery clients.

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Fitted modelBi-monthly proportion whomake complete recovery


Pr(

Com

ple

te r

eco

very

at

6 m

ths)


Figure 2: Impact of the Recovery model on functional outcomes (GOS-E complete

recovery) at 6-months post-injury of VOTOR clients


4.2.4 Impact of the Recovery model on return to work outcomes at 6-months

The probability of return to work was stable prior to introduction of the Recovery model, followed

by a significant decline at the point of implementation and a general upward trend in return to

work since the Recovery model was implemented (Table 4 and Figure 3).

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Fitted modelBi-monthly proportion whoreturn to work


Pr(

Retu

rn t

o w

ork

at

6 m

ths)


Figure 3: Impact of the Recovery model on return to work at 6-months post-injury of

VOTOR clients


4.2.5 Impact of the Recovery model on reporting moderate to severe pain at 6-months

The probability of VOTOR TAC clients reporting moderate to severe persistent pain at 6-months

was largely stable prior to the implementation of the Recovery model, did not change at the

point of implementation and has been largely stable since the introduction of the Recovery

model (Table 4 and Figure 4).

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Fitted modelBi-monthly proportion withmoderate/severe pain


Pr(

Mod

era

te/s

eve

re p

ain

at

6 m

ths)


Figure 4: Impact of the Recovery model on reporting moderate to severe persistent pain

at 6-months post-injury of VOTOR clients


4.2.6 Impact of the Recovery model on EQ-5D outcomes at 6-months

There was a non-significant decline in the probability of reporting mobility problems prior to

introduction of the Recovery model, followed by a small increase at the point of implementation,

and a continuing decline since the introduction of the Recovery model (Figure 5). The decline

since the change in claims model was not significant as this pattern was consistent with the

period prior to implementation (Table 4 and Figure 5).

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Fitted modelBi-monthly proportionwith some mobility problems


Pr(

Som

e m

ob

ility

pro

ble

ms a

t 6 m

ths)

Figure 5: Impact of the Recovery model on reporting problems with mobility on the EQ-

5D at 6-months post-injury of VOTOR clients


Similar to the mobility item of the EQ-5D, the probability of reporting problems on the EQ-5D

self-care item at 6-months was stable prior to Recovery model implementation, increased

significantly at the point of implementation and has shown a non-significant decline since

(Figure 6 and Table 4).

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Fitted modelBi-monthly proportionexperiencing problemswith self-care


Pr(

Pro

ble

ms w

ith s

elf-c

are

at

6 m

ths)

Figure 6: Impact of the Recovery model on reporting problems with self-care on the EQ-

5D at 6-months post-injury of VOTOR clients


The probability of reporting problems on the EQ-5D usual activities item at 6-months was stable

to Recovery model implementation, increased marginally at the point of implementation and has

been showing a stable trend since (Figure 7 and Table 4).

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Fitted modelBi-monthly proportionexperiencing problemswith usual activities


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ctivitie

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t 6 m

ths)

Figure 7: Impact of the Recovery model on reporting problems with usual activities on

the EQ-5D at 6-months post-injury of VOTOR clients


The probability of reporting problems on the EQ-5D pain or discomfort item at 6-months was

declining prior to Recovery model implementation, increased at the point of implementation and

has been showing a strong downward trend since. However, as the slope “before” indicated a

decline in the probability of reporting pain or discomfort and the slope afterwards is showing a

not dissimilar gradient of slope, the post-Recovery model implementation improvement is not

significant (Figure 8 and Table 4). This indicates that the introduction of the Recovery model is

unlikely to explain the improvement observed.

.5.5

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.85

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Fitted modelBi-monthly proportionexperiencing problemswith pain/discomfort


Pr(

Pro

ble

ms w

ith p

ain

/dis

com

fort

at

6 m

ths)

Figure 8: Impact of the Recovery model on reporting problems with pain or discomfort on

the EQ-5D at 6-months post-injury of VOTOR and VSTR clients


The probability of reporting problems on the EQ-5D anxiety or depression item at 6-months was

increasing marginally prior to Recovery model implementation, relatively stable at the point of

implementation and has been shown towards improved outcomes on this items since, as

evidenced by the downward slope in the post-Recovery phase and a p-value approaching

significance (Table 4 and Figure 9).

.3.3

5.4

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.5.5

5.6

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ov 2

008

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009

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Fitted modelBi-monthly proportionexperiencing problemswith anxiety/depression


Pr(

Pro

ble

ms w

ith a

nxie

ty a

t 6 m

ths)

Figure 9: Impact of the Recovery model on reporting problems with anxiety or

depression on the EQ-5D at 6-months post-injury of VOTOR and VSTR clients


The figure for the EQ-5D summary score at 6-months post-injury is provided as Figure 10, and

shows a stable trend of mild improvement over time prior to the introduction of the Recovery

model, little change at the point of implementation, and continuation of the pre-Recovery pattern

after implementation of the Recovery model. There is no evidence that the Recovery model has

improved EQ-5D utility scores (Table 4 and Figure 10).

.45

.5.5

5.6

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.7.7

5

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008

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Fitted modelBi-monthly meanEQ-5D summary score


EQ

-5D

Su

mm

ary

sco

re a

t 6

mth

s


Figure 10: Impact of the Recovery model on the EQ-5D summary score at 6-months post-

injury of VOTOR clients


4.2.7 Impact of the Recovery model on SF-12 outcomes at 6-months

Figure 11 shows the trend in mean PCS-12 scores at 6-months post-injury before and after

implementation of the Recovery model. Mean PCS-12 scores were stable prior to

implementation, unchanged at implementation and stable since the implementation of the

Recovery model (Figure 11 and Table 4).

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Fitted modelBi-monthly meanPCS-12


PC

S-1

2 a

t 6

mth

s


Figure 11: Impact of the Recovery model on physical health at 6-months post-injury of

VOTOR clients


Consistent with the PCS-12 findings, there was no evidence that the Recovery model has

impacted on MCS-12 scores in the VOTOR population (Table 4 and Figure 12). 40

45

50

55

60

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ov 2

007

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Fitted modelBi-monthly meanMCS-12


MC

S-1

2 a

t 6

mth

s


Figure 12: Impact of the Recovery model on mental health at 6-months post-injury of

VOTOR clients


4.2.8 Impact of the Recovery model on total TAC claim costs at 6-months

The result of the segmented regression modelling for modelling 6-month claim costs is shown in

Figure 13. Before implementation of the Recovery model, claim costs were stable. At the point

of implementation of the Recovery model, there was evidence of a marginal drop in claim costs

(Table 4). Since introduction of the Recovery model, claim costs appear to be increasing

though the increase is not significant (Figure 13 and Table 4).

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Fitted modelBi-monthly geometric meanCosts($)


Co

sts

($)

at

6 m

ths


Figure 13: Impact of the Recovery model on 6-month post-injury claim costs for VOTOR

clients


4.3 Impact of the implementation of the Recovery model on 12-month

outcomes

4.3.1 Summary of 12-month outcomes in the pre-Recovery and post-Recovery periods

Table 4 shows the outcomes at 12-months post-injury the Pre-Recovery and Post-Recovery

groups. Again, there was consistency in patient-reported and 12-month claim cost outcomes

when simply comparing the cases before the Recovery model was implemented with the cases

since the Recovery model was implemented (Table 5). The EQ-5D was excluded from the 12-

month outcomes assessment due to low numbers of patients in the pre-Recovery timeframe

related to the late inclusion of this outcome measure in the VOTOR follow-up protocol.

Table 5: Comparison of outcomes pre and post Recovery model at 12-months

N

Pre-Recovery Outcome*

N (%)

N

Post-Recovery Outcome

N (%)

Good recovery 2,194 821 (37.4) 3,158 1,153 (36.5)

Return to work** 1,630 1,110 (68.1) 2,245 1,548 (69.0)

Complete recovery 2,194 429 (19.6) 3,158 610 (19.3)

Moderate/severe persistent pain 1,701 450 (26.5) 2,457 643 (26.2)

Mean (SD) Mean (SD)

PCS-12 1,671 40.8 (11.9) 2,390 40.4 (12.3)

MCS-12 1,671 48.8 (11.7) 2,390 49.7 (11.9)

Cost ($) 2,511 72,993 (69,472) 3,097 73,622 (68,824)

* Similarly patients with a date of injury in the 12 months prior to the introduction of the Recovery model are excluded from the 12 month summary; ** If working prior to injury.

4.3.2 Summary of the findings from the segmented regression modelling at 12-months

The results of the multivariable segmented regression modelling to quantify the impact of the

introduction of the Recovery model on 12-month outcomes of VOTOR clients are summarised in

Table 6. The baseline period refers to before the introduction of Recovery model. The log odds

of the baseline trend showed stability in all studied outcomes (Table 6). At the point of

implementation, there was a significant increase in the log-odds of reporting a complete

recovery on the GOS-E, and a near significant 8 per cent reduction in claims costs. Compared

to the pre-Recovery phase, the post-Recovery probability of reporting moderate to severe

persistent pain declined, while the total claim costs showed a significant increase (Table 6).


Table 6: Impact of the introduction of the Recovery model on 12-month outcomes – results from the multivariable* segmented regression

Outcome Baseline trend

Log-odds (95% CI)

p-value Level change after Recovery

Log-odds (95% CI)

p-value Trend change after Recovery

Log-odds (95% CI)

p-value

Good recovery -0.02 (-0.04, -0.003) 0.12 0.02 (-0.22, 0.26) 0.88 0.02 (-0.01, 0.05) 0.15

Complete recovery -0.01 (-0.04, 0.01) 0.33 0.32 (0.03, 0.61) 0.03 -0.01 (-0.04, 0.02) 0.59

Return to work** 0.01 (-0.02, 0.04) 0.53 -0.13 (-0.42, 0.16) 0.61 0.001 (-0.03, 0.03) 0.94

Moderate/severe persistent pain 0.02 (-0.01, 0.05) 0.13 0.03 (-0.26, 0.33) 0.83 -0.04 (-0.07, -0.01) 0.02

Baseline trend

(95% CI)


(95% CI)


(95% CI)

PCS-12 -0.08 (-0.22, 0.05) 0.21 -0.77 (-2.20, 0.67) 0.29 0.16 (0.01, 0.32) 0.04

MCS-12 -0.001 (-0.13, 0.13) 0.99 0.55 (-0.89, 1.98) 0.46 -0.01 (-0.16, 0.15) 0.93

Baseline trenda

(95% CI)

Level change after Recoveryb

(95% CI)

Trend change after Recoveryc

(95% CI)

Cost ($) -0.1 [-0.9, 0.7] 0.89 -8.0 [-16.3, 1.1] 0.08 1.1 [0.1, 2.2] 0.04

n.b. Patients with a date of injury 12 months prior to the introduction of the Recovery model were excluded from the analysis because their care would be a mixture of the old model and the Recovery model; *All analyses were adjusted for age, gender, road user group, injury group, head injury, chest or abdominal injury, other injury, comorbid status, pre-injury work status, geographic remoteness and socioeconomic status; a Percentage change in 12 month costs per 2 month interval; b

Percentage change in 12 month costs immediately after Recovery was introduced; c Percentage change in trend after Recovery compared to percentage change before Recovery was introduced.


4.3.3 Impact of the Recovery model on functional outcomes at 12-months (GOS-E)

Figure 14 and Figure 15 represent the GOS-E good recovery and complete recovery outcomes at

12-months, respectively. There was a trend for declining probability of a good functional recovery

prior to implementation of the Recovery model, slight improvment at the point of implementation

and then a stablisation of the probability of reporting a good recovery after the Recovery model

introduction (Figure 14 and Table 6).

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Fitted modelBi-monthly proportion whomake good recovery


Pr(

Go

od

re

cove

ry a

t 1

2 m

ths)


Figure 14: Impact of the Recovery model on functional outcomes (GOS-E good recovery) at

12-months post-injury of VOTOR clients


Figure 15 shows a slight improvement in the probability of a complete functional recovery at 12-

months prior to the introduction of the Recovery model, a small improvement at the point of

implementation, and a generalised downward trend since introduction of the Recovery model

(Figure 15). However, there was no evidence of significant impact of the Recovery model on

reporting a complete functional recovery at 12-months (Table 6).

.05

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Fitted modelBi-monthly proportion whomake complete recovery


Pr(

Com

ple

te r

eco

very

at

12

mth

s)


Figure 15: Impact of the Recovery model on functional outcomes (GOS-E complete

recovery) at 12-months post-injury of VOTOR clients


4.3.4 Impact of the Recovery model on return to work at 12-months

There appears to be little evidence of an impact on 12-month return to work rates for VOTOR

clients following implementation of the Recovery model (Table 6 and Figure 16). The rate of return

to work appeared to be improving prior to Recovery model implementation, decreased marginally

at the point of implementation and then has shown a slight upward trajectory since implementation

of the Recovery model (Figure 16). However, these trends were not significant (Table 6).

.55

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Fitted modelBi-monthly proportion whoreturn to work


Pr(

Retu

rn t

o w

ork

at

12

mth

s)


Figure 16: Impact of the Recovery model on return to work at 12-months post-injury of

VOTOR clients


4.3.5 Impact of the Recovery model on reporting moderate to severe pain at 12-months

The probability of reporting moderate to severe persistent pain at 12-months was stable prior to the

implementation of the Recovery model, changed little at the point of implementation, and has

shown a significant decline (i.e. less pain) since implementation of the Recovery model (Figure 17

and Table 6).

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Fitted modelBi-monthly proportion withmoderate/severe pain


Pr(

Mod

era

te/s

eve

re p

ain

at

12 m

ths)


Figure 17: Impact of the Recovery model on reporting moderate to severe persistent pain at

12-months post-injury of VOTOR and VSTR clients


4.2.6 Impact of the Recovery model on SF-12 outcomes at 12-months

The figure for the PCS-12 scores at 12-months post-injury is provided as Figure 18. Physical

health scores were declining marginally prior to the Recovery model implementation, did not

change at the point of implementation and have shown a significant upward trend (i.e. improved

physical health) since the introduction of the Recovery model (Table 6 and Figure 18).

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Fitted modelBi-monthly meanPCS-12


PC

S-1

2 a

t 1

2 m

ths


Figure 18: Impact of the Recovery model on physical health at 12-months post-injury of

VOTOR clients


The figure for the MCS-12 scores at 12-months post-injury is provided as Figure 19. There is no

evidence of a change in mental health scores at 12-months following implementation of the

Recovery model (Figure 19 and Table 6). 40

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Fitted modelBi-monthly meanMCS-12


MC

S-1

2 a

t 1

2 m

ths


Figure 19: Impact of the Recovery model on mental health at 12-months post-injury of

VOTOR clients


4.2.7 Impact of the Recovery model on TAC claim costs at 12-months

The results of the segmented regression modelling 12-month claim costs as the outcome is shown

in Figure 20. Before implementation of the Recovery model, 12-month claim costs were stable. At

the point of implementation of the Recovery model, there was evidence of significant drop in claim

costs (Table 6 and Figure 20). Since introduction of the Recovery model, claim costs have

increased significantly (Figure 20 and Table 6).

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Fitted modelBi-monthly geometric meanCosts($)


Co

sts

($)

at

12

mth

s


Figure 20: Impact of the Recovery model on 12-month post-injury claim costs for VOTOR

clients


5. Summary of findings

The analyses presented in this report provide an updated, and extended, analysis of the impact of

the Recovery model presented in Research Report # 0213-007-R4C and Research Report # 0214-

007-R6. The updated linkage allowed a final analysis of the impact of the initial implementation of

the Recovery model. Overall, there is little consistent evidence that the Recovery model has

improved patient-reported outcomes for hospitalised orthopaedic trauma patients, particularly at 6-

months post-injury. At 12-months post-injury, there was evidence that the probability of reporting

moderate to severe pain has decreased since the model was introduced, and physical health

scores have improved. However, the introduction of the Recovery model was associated with

increased claim costs at 12-months.

Through discussion with key TAC personnel, the largely null findings are consistent with internal

assessments and the conclusion was that initiatives introduced as part of the Recovery model

were not expected to impact on client outcomes. A focus of the Recovery model was the capacity

to segment clients into streams of claim management based on the complexity of their needs or

circumstances, with the segmentation largely focused on the risk of lodging a common law claim.

Recovery Branch processes changed considerably since the implementation of the model, making

it difficult to evaluate as a single intervention. Most importantly, the introduction of mental health

screening and other interventions were not introduced during the evaluation timeframe, limiting the

capacity to impact on client outcomes.

6. Implications for the TAC

The analyses presented in this report support internal TAC assessments of the impact of the

Recovery model on client outcomes. There has been no clear or consistent improvement in client

outcomes following implementation of the Recovery model. The initiatives that should be expected

to impact demonstrably on client outcomes were implemented in late 2013 and early 2014 (e.g.

First Service and Mental Health screening should provide demonstrable improvements in client

outcomes and can be assessed in future analyses.

Documents

083 VOTOR-TAC Linkage Report - Impact of the TAC Recovery Model