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The Impact of Co-morbidity 2 nd ACHRF Auckland, New Zealand 8 November 2012 Dr John Wren Principal Research Advisor ACC Dr Barry Gribben CBG Health Research Lauren Prosser Senior Policy Advisor ACC ACC Service Utilisation & Costs 2012 - 2025

Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

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Page 1: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

The Impact of Co-morbidity

2nd ACHRF Auckland, New Zealand 8 November 2012

Dr John Wren

Principal Research Advisor

ACC

Dr Barry Gribben

CBG Health Research

Lauren Prosser

Senior Policy Advisor

ACC

ACC Service Utilisation & Costs 2012 - 2025

Page 2: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

The Questions

WHAT is the effect of a health co-morbidity on ACC clients ?

˃ injury treatment claim rates (utilisation)

˃ duration of claim

˃ costs over time

WHAT are the cost effects on an aging population ?

WHAT are the policy implications ?

Page 3: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

The Process

BUILT on the pilot studies reported in 2010 (Wren & Mason)

LINKED Primary Health Care data (GP Practice) with

Ministry of Health & ACC data using New Zealand NHI

˃ Random sample of 337,665 people

˃ Sample representative of the New Zealand population

Descriptive & Multivariate Statistical Analysis

Page 4: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Co-morbidities

Asthma

Chronic obstructive pulmonary disease

Ischaemic heart disease

Heart failure

Diabetes mellitus

Mental health condition

Cancer diagnosis

Osteoarthritis

Hypertension

Page 5: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Variables & Interactions

Age

Sex

Ethnicity

Socio-economic status (New Zealand social deprivation index)

Treatment utilisation

Claims duration

ACC Costs

Page 6: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Headline Statistical Results

Multivariate Model Analysis

Presence of a health co-morbidity was found to have a strong

statistically significant (95%) association with:

˃ increased service utilisation

˃ higher costs

The effects were independent of, and additional to, normal

health cost effects typically associated with age, gender,

ethnicity & socio-economic status

Page 7: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Headline Statistical Results

Presence of one or more health co-morbidities showed …

˃ 28% more claims

˃ 346% higher lump sum payments

˃ 59% higher medical treatment costs

˃ 39% more weekly compensation costs

OVERALL 59% more total ACC cash costs

across all cost categories

Page 8: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

… stronger relativity for some than others

Average total cost per person per year (95% CI)

Cost relativity

0

200

400

600

800

1000

1200

1400

NoYesNoYesNoYesNoYesNoYesNoYesNoYesNoYesNoYes

DiabetesAsthmaCancerHeart FailureIHDMental HealthHyper-tensionStrokeOsteo-arthritis

Average total cost per annum NZD

Page 9: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Claim Utilisation

0.00

0.50

1.00

1.50

2.00

2.50

0 1 2 3 4 5 6 7

Claims

per

annum

Number of co-morbidities present

Claims Utilisation vs. Number of Co-morbidities

Page 10: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Medical Treatment Cost

$-

$50

$100

$150

$200

$250

$300

$350

$400

$450

$500

0 1 2 3 4 5 6 7

Medical

Treatment

Cost

Number of co-morbidities present

Medical Treatment Costs vs. Number of Co-morbidities

Page 11: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Total Annual Cost

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

0 1 2 3 4 5 6 7

Total

Annual

Cost

Number of co-morbidities present

Total Annual Cost vs. Number of Co-morbidities

Page 12: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Total Annual ACC Cost is

attributable to

presence of co-morbidities in the

New Zealand population

$276 million (NZD, 2011)

Based on the analysis

10.7%

Page 13: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Me

an

co

st

pe

r a

nn

um

pe

r p

ers

on

Age group

Average total cost per person Age group co-morbidity vs. no co-morbidity

Mean $ NO co-morbidity

Mean $ co-morbidity

Ageing Effects

Excess cost is the area

of the gap between the

two lines – largest gap is

in the working age

population

Page 14: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85-90

90+

Nu

mb

er

of

pe

op

le

Agegroup

Population by age group with co-morbidity vs no co-morbidity

Withoutco-morbidity

Withco-morbidity

Modelling Ageing Effects to 2025

Expect this area to

get bigger

Page 15: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Costs Attributable to Co-morbidities

2012 2015 2020 2025

no change 10.66% 10.67% 10.96% 11.25%

1% growth 10.71% 10.83% 11.43% 12.06%

2% growth 10.77% 11.00% 11.91% 12.72%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Perc

en

tag

e o

f to

tal A

CC

co

sts

Page 16: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Conclusions

Presence of a wide range of health co-morbidities in the

population has a real effect on injury compensation

treatment utilisation volumes and costs

To 2025, 10.7% to 12.7% of total annual ACC costs is

estimated to be attributable to presence of co-morbidities in

the population

It appears that although aging of the population means more

people have co-morbidities, this is counterbalanced by

relatively fewer people being in the age groups where the

cost differences are greater

Results are consistent with recent research from NCCI in

America about working age effects

Page 17: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Gribben, B. & Wren, J. ( 2012) The Impact of Health

Comorbidities on ACC Injury Treatment and Rehabilitation

Utilisation and Costs, and cost estimate to 2025 of effects in

an aging population.

CBG Health Research and ACC Research, Sep 2012.

Wren, J. & Mason, J. 2010. Results of Three Pilot Studies

Exploring & Quantifying Health Co-morbidity Effects on ACC

Injury Treatment Utilisation and Costs.

ACC.

For further information

Page 18: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Additional Slides

Page 19: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Health Literature

Pre-existing health co-morbidity effects on increased health

service utilisation well-documented in recent World Health

Organisation (WHO) reports

˃ Injured people are different from the non-injured population in terms

of pre-existing morbidity

˃ Patients with higher numbers of co-morbidities utilise injury services

more than patients with lower co-morbidities.

Cameron, Prudie, Kliewer et al., 2005)

Page 20: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Health co-morbidity (ICD-

9-CM Chapter)

Rate Ratios* Injured/ Non-Injured

*Adjusted for age, sex and place of

residence *

Source: Adapted from Cameron et al, 2005. Tables 4 and 5 respectively. Hospital Admissions Physician

Claims per 1000 person years

Mental Health disorders 9.31 3.50

Injury and poisonings 3.68 2.72

Blood diseases 3.36 1.53

Endocrine and metabolic 2.79 1.38

Musculoskeletal disorders 2.61 1.76

Nervous system diseases 2.35 1.42

Respiratory diseases 1.98 1.38

Circulatory diseases 1.70 1.21

Page 21: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Role of Mental Health, Alcohol and Psychological Traits

“There appears to an aetiological link between mental health

conditions and injury, particularly in relation to risk-taking

behaviours, alcohol misuse, and psychological traits such as

impulsivity, sensation-seeking, and risk-perception.”

(Cripps & Harrison, 2008. Briefing report for the Australian Institute of Health and Welfare)

Health Literature

Page 22: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Increased injury risks, higher medical treatment costs

(including pharmaceutical services), workers compensation

costs, and poor work performance (presenteeism) have

consistently been associated with specific lifestyle risk

factors such as tobacco use (current and previous), obesity,

stress, and lack of regular physical activity among working

people in a variety of settings

(Studies published by Health Management Research Centre, and Others)

Workers Compensation Literature

Page 23: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Workers Compensation Literature

Considerable confidence the excess risk from health co-

morbidities accounts for at least 25% to 30% of medical

costs per year across a wide variety of companies,

regardless of industry or demographics

The biggest cost factors are the cost of extra treatment

utilisation, and medical costs associated with the

complications of a co-morbidity

(Studies published by Health Management Research Centre)

Page 24: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

ACC Claims Costs

Highly skewed

Page 25: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

All Results Significant at 95%

Page 26: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Multivariate Analysis

Page 27: Results of Acc Modeling of Comorbidity and Ageing Effects Barry Gribben ACHRF 2012

Future Cost Calculation

The proportion of ACC costs attributable to chronic

illness in any given out year is a function of:

the population structure (the matrix Nij)

the number of years from our baseline, n.

Pij, r, $ccij and $nccij are all constants calculated

earlier, or assumed.

ij

n

ijij

n

ijijij

ij

ijij

n

ijij

iPncciPccN

ncccciPN

nNfP))1(1($)1(($

)$($)1(

),(