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The economic costs of heart attack and chest pain (Acute Coronary Syndrome)

Acknowledgements

Access Economics would like to acknowledge with appreciation the insightful comments and guidance received from various people in the development of this report, including Professor Derek Chew, Associate Professor David Brieger, Dr Ren Tan, Jenny Coutts, Margaret Flaherty, Ineke Bleeker, Dr Alex Brown, Graham Neville, Tony Arvidsson, Rohan Greenland, Dr Andrew Boyden, Kim Goodman, Debbie White, Associate Professor Paul Middleton, Linda Soars, members of the Cardiology Advisory Board (Eli Lilly), Dr Deon Gouws, Paul Dale, Stuart Englund and Fiona Bailey. We would especially like to thank Emeritus Professor Michael Hobbs for providing access to unpublished research information and advice on the epidemiology of ACS and its treatment in Perth.

This report aims to enhance the understanding of, and reiterate, the growing impact of Acute Coronary Syndrome on Australia and the need for every effort to be made to resolve the treatment gaps.

Copyright Data relating to the WA linked database supplied by Emeritus Professor Michael Hobbs and presented in this report are unpublished from research in progress. They may not be provided to, or published by, third parties without the permission of Professor Michael Hobbs.

To obtain a copy

A copy of ‘The economics costs of heart attack and chest pain (Acute Coronary Syndrome)’ can be downloaded from www.accesseconomics.com.au/publicationsreports.php

The economic costs of heart attack and chest pain (Acute Coronary Syndrome) June 2009

While every effort has been made to ensure the accuracy of this document, the uncertain nature of economic data, forecasting and analysis means that Access Economics Pty Limited is unable to make any warranties in relation to the information contained herein. Access Economics Pty Limited, its employees and agents disclaim liability for any loss or damage which may arise as a consequence of any person relying on the information contained in this document.


Executive Summary

Access Economics was commissioned by Eli Lilly to estimate the economic costs of Heart Attack and Chest Pain (Acute Coronary Syndrome-ACS) in Australia for 2009. In addition, an objective was to investigate current gaps in ACS treatment and clinical need, and highlight areas of treatment where further investment may result in significant benefits through a reduction in the burden of disease and improvements in efficiency and quality of care.

To estimate the economic costs of ACS, this study has used the comprehensive cost of illness framework used throughout the world. In brief, the study consists of the following sections:

■ epidemiology of ACS in Australia;

■ direct health care system costs associated with treatment;

■ indirect financial and economic costs;

■ value of the loss in health associated with morbidity and mortality; and

■ the future of ACS management in Australia.

Unless ACS leads to immediate death, patients experiencing an ACS event are hospitalised. Data on hospitalisations and death were used to estimate the number of ACS events in Australia. As the Australian Institute of Health and Welfare (AIHW) data does not account for readmission and transfers, 28 day age standardised separation rates were sourced from the Western Australian linked dataset with the assistance of Emeritus Professor Michael Hobbs. These were extrapolated to the Australian setting using projected Australian population data.

It is projected that in 2009 there will be around 79,990 hospitalisation associated with ACS, of which 59% is expected to be due to heart attack (AMI), and the remaining associated with chest pain (unstable angina). Table i shows the projected number of hospitalisations by gender and condition for 2009.

Table i: Projected number of ACS hospitalisations in Australia 2009

Unstable angina AMI ACS

Male 20,224 28,596 48,820

Female 12,228 18,943 31,170

Total 32,452 47,539 79,990

Source: Access Economics calculations

Some hospitalisations due to heart attacks are likely to be followed by death. However, deaths following a hospitalisation are expected to account for only 24% of all deaths associated with heart attacks. Most deaths will occur before a person can be admitted to hospital. In total, it is expected that 9,959 people will die from a heart attack in 2009, of which 2,423 are expected to occur within 28 days of an admission. Projected deaths following a heart attack by gender and age for 2009 are shown in Table ii.


Table ii: Projected number of deaths following a heart attack in Australia 2009

Males Females Total

35–44 years 81 18 99

45–54 years 239 44 283

55–64 years 478 137 615

65–74 years 892 428 1,320

75–84 years 1,905 1,570 3,475

85 years and over 1,427 2,741 4,167

Total 5,022 4,937 9,959

Source: ABS (2003, 2004, 2005, 2006 and 2007) and Access Economics calculations

The projected number of hospitalisations and deaths associated with ACS means the direct health care system costs, indirect costs, and burden of disease imposed on society will be significant. Table iii presents a summary of projected hospitalisations, deaths and economic costs associated with ACS, split into various cost components, for 2009.

It is projected that the number of ACS hospitalisations and deaths will be 87,526 in 2009 with an associated total economic cost of $17.9 billion. Of this, direct health care system costs (primarily hospital stays and pharmaceuticals) are expected to account for around $1.8 billion. Indirect costs are expected to account for $3.8 billion, primarily due to lost productivity. The largest cost is expected to be the loss in the value of health, otherwise known as the burden of disease due to morbidity and mortality. It is expected that due to disability imposed on individuals, and the loss of life associated with premature mortality, the value in the loss of health will be approximately $12.3 billion in 2009.

In total, heart attacks are expected to cost around $15.5 billion in 2009. The majority of these costs are associated with the loss in the value of health, accounting for around 78%, which is representative of the large amount of premature deaths associated with heart attacks. Total direct health care system costs and indirect costs are expected to total around $3.5 billion in 2009. The total cost per heart attack is expected to average $281,000.

Unstable angina (chest pain at rest) is expected to cost around $2.4 billion in 2009. However the burden of disease only comprises $311 million, or around 13%. The majority of costs are associated with direct and indirect costs, totalling around $2.1 billion. The total cost per unstable angina event is expected to average $74,000.


Table iii: Summary of estimated separations, deaths and costs 2009

Heart attack Chest pain ACS

Deaths before reaching a hospital 7,536 0 7,536

Hospitalisations without deatha 45,115 32,452 77,567

Hospitalisations with death occurring later 2,423 0 2,423

Total hospitalisations 47,538 32,452 79,990

Total Events 55,074 32,452 87,526

$ (million) $ (million) $ (million)

Direct health care system costs 1,191 577 1,767

Productivity loss (reduced participation) 1,254 1,073 2,327

Productivity loss (premature mortality) 287 0 287

Informal care 411 280 691

Deadweight loss 328 159 486

Burden of disease (YLD) 719 311 1,030

Burden of disease (YLL) 11,307 0 11,307

Total costs 15,497 2,400 17,895

$ $ $

Cost per separation (direct costs only)b 25,000 18,000 22,000

Cost per event (all costs)b

281,000

74,000

204,000

Note: (a) Within 28 days of being admitted to hospital (b) Cost per hospitalisation and cost per event have been rounded to the nearest $1,000. Source: Access Economics

This study has also highlighted gaps in the treatment and monitoring of ACS throughout Australia. These include:

a national ACS registry managed by an independent body that includes comprehensive and consistent data on patients, treatment, and rehabilitation services Australia-wide; which can be used to develop a common set of performance indicators and ACS treatment outcome measures;

a national approach to cardiac rehabilitation, including inpatient, outpatient and maintenance care, specific strategies to increase the uptake of women into rehabilitation, further investment to ensure rehabilitation programs are accessible to all regardless of income and geographical location;

an increase in the compliance and adherence with medication via the Quality Use of Medicines program;

a standardised national program to support employees and employers and the extension of rehabilitation practices. Workplaces can provide an excellent environment to facilitate the ongoing rehabilitation and lifestyle changes to prevent the re-occurrence of ACS events; and


further research into the optimal use of existing therapies and identification and promotion of cost effective treatments currently being used within other health systems throughout the world.

The focus on ACS at this point in time is particularly important in the context of demographic ageing in Australia, given the increasing age standardisation rates among the older population and the link between health, health care resource utilisation, and quality of life. In 2010, the first of the baby boomers will reach the age of 65 years, where the risk of ACS significantly increases. It is expected that the proportion of the Australian population that is 65 years and older (and therefore at higher risk of an ACS event) will increase from around 14% in 2009 to around 23% in 2050. This, coupled with the expected increase in risk factors associated with ACS such as obesity and diabetes, means public and private health care resources to prevent and treat ACS are expected to come under significant pressure in the near future.

To mitigate these pressures, investment in cost effective programs should be undertaken now to improve effectiveness and efficiency of ACS treatment in the future. The first step should be to invest in the collection and dissemination of information and data associated with treatment across Australia at a local level. Informed analysis should then be undertaken to identify differences in treatment paths, to determine optimal therapies, and to inform best practice.

The goal of ACS management should be to shift resources to cost effective technologies, thereby improving the efficiency of ACS treatment and generating greater health benefits for the Australian community. To ensure any gains made in the hospital are not undone once the patient steps out the hospital door, monitoring of health outcomes, individual behaviours, and the effectiveness of rehabilitation also needs to be measured, continually monitored and supported.

Access Economics

June 2009


Contents

Executive Summary.......................................................................................................................4

1 The epidemiology of ACS in Australia ............................................................................... 12

1.1 Definition of ACS .................................................................................................................. 12

1.2 Development of ACS ............................................................................................................ 14

1.3 Risk factors and comorbidity associated with ACS .............................................................. 15

1.4 Projected number of ACS events in Australia ...................................................................... 23

1.5 Impact of demographic ageing ............................................................................................ 36

2 Direct health care system costs ........................................................................................ 39

2.1 Methodology ........................................................................................................................ 39

2.2 Direct health care system costs ........................................................................................... 40

2.3 Trends in direct health care system costs ............................................................................ 44

2.4 Direct health care system cost per separation .................................................................... 45

3 Indirect costs associated with ACS .................................................................................... 47

3.1 Productivity losses ............................................................................................................... 47

3.2 Cost of informal care ............................................................................................................ 50

3.3 Private costs associated with rehabilitation ........................................................................ 52

3.4 Deadweight loss associated with public funding of health care .......................................... 52

4 Burden of disease ............................................................................................................. 54

4.1 Methodology used for measuring and valuing the burden of disease ................................ 54

4.2 Burden of disease from ACS ................................................................................................. 55

4.3 Burden of disease comparisons ........................................................................................... 57

5 Summary of costs .............................................................................................................. 58

6 The future of ACS management ........................................................................................ 59

6.1 A multidisciplinary approach to ACS care ............................................................................ 59

6.2 A national ACS registry ......................................................................................................... 60

6.3 Rehabilitation ....................................................................................................................... 62

6.4 Next generation antiplatelet agents .................................................................................... 65

Appendix A: Epidemiology estimates and projections................................................................ 69

References ................................................................................................................................... 76

Charts

Chart 1.1 : Share of CHD deaths by risk factors 2003 ................................................................ 17

Chart 1.2 : Share of CHD DALYs by risk factors 2003 ................................................................. 17

Chart 1.3 : Risk of AMI associated with exposure to multiple risk factors ................................. 18

Chart 1.4 : Reduced risk of AMI associated with healthy behaviour ......................................... 18

Chart 1.5 : Trends in daily smoking for those aged 14 years and over ...................................... 19

Chart 1.6 : Prevalence of overweight and obese people in Australia ........................................ 20

Chart 1.7 : Trend in blood pressure amongst Australians aged 25 to 64 ................................... 21


Chart 1.8 : Trends in diabetes within Australia .......................................................................... 22

Chart 1.9 : Actual and projected age standardised separation rates for AMI ........................... 26

Chart 1.10 : Actual and projected age standardised separation rates for unstable angina ...... 26

Chart 1.11 : Actual and projected age standardised separation rates for ACS .......................... 27

Chart 1.12 : Projected age standardised separation rates by condition 2009 ........................... 28

Chart 1.13 : Projected age standardised separation rates for ACS 2009 ................................... 28

Chart 1.14 : Comparison of projected ACS separations in Australia 2009 ................................. 30

Chart 1.15 : Projected male separations in Australia 2009 ........................................................ 31

Chart 1.16 : Projected female separations in Australia 2009..................................................... 32

Chart 1.17 : Projected total separations in Australia by condition 2009 ................................... 32

Chart 1.18 : Projected total separations in Australia by gender 2009 ....................................... 33

Chart 1.19 : Share of AMI and angina pectoris across states and territories 2006-07 .............. 34

Chart 1.20 : 28 day case fatality following AMI .......................................................................... 35

Chart 1.21 : Actual and projected deaths following AMI Australia ............................................ 36

Chart 1.22 : Projected Australian population age structure ....................................................... 37

Chart 1.23 : Projected ACS separations in Australia .................................................................. 38

Chart 2.1 : Distribution of direct health care system costs of ACS 2009 ................................... 42

Chart 2.2 : Direct health care system costs of ACS by expenditure type 2009 .......................... 43

Chart 2.3 : Direct health care system costs of AMI by expenditure type 2009 ......................... 43

Chart 2.4 : Direct health care system costs of unstable angina by expenditure type 2009....... 44

Chart A.1: Male AMI separation rates and trends ...................................................................... 71

Chart A.2: Female AMI separation rates and trends ................................................................... 72

Chart A.3: Male unstable angina separation rates and trends ................................................... 72

Chart A.4: Female unstable angina separation rates and trends ................................................ 73

Tables

Table 1.1 : Definition of ACS used in this study .......................................................................... 14

Table 1.2 : Prevalence distributions for seven lifestyle risk factors by age and sex 2003 ......... 16

Table 1.3 : ACS age standardised separations per 100,000 in the Perth Statistical Division ..... 24

Table 1.4 : Projected deaths following AMI by age bracket Australia 2009 .............................. 36

Table 2.1 : Projected direct health care system costs by age and gender 2009 ........................ 41

Table 2.2 : Projected direct health care system costs, by expenditure type 2009 .................... 42

Table 2.3 : Patient days associated with unstable angina and AMI ........................................... 45

Table 2.4 : Trend in direct health care system costs associated with ACS .................................. 45

Table 2.5 : Direct health care system costs per separation 2009 ............................................... 46


Table 3.1 : Productivity loss due to premature death 2009 ....................................................... 49

Table 3.2 : Productivity loss due to working days lost 2009 ...................................................... 50

Table 4.1 : Value of YLDs associated with ACS 2009 .................................................................. 56

Table 4.2 : YLLs from ACS 2009 .................................................................................................. 56

Table 4.3 : Burden of disease in Australia 2009 ......................................................................... 57

Table 5.1 : Summary of separations, deaths and costs 2009 ..................................................... 58

Table 6.1 : Factors that impact on health and health outcomes ................................................ 61

Table 6.2 : Recommended medications for ACS treatment ........................................................ 64

Table 6.3 : Status of new antiplatelet agents ............................................................................. 67

Table A.1: Male AMI age standardised separations per 100,000 ............................................... 69

Table A.2: Female AMI age standardised separations per 100,000 ............................................ 69

Table A.3: Male unstable angina age standardised separations per 100,000 ............................ 70

Table A.4: Female unstable angina age standardised separations per 100,000 ......................... 70

Table A.5: Male ACS age standardised separations per 100,000 ................................................ 70

Table A.6: Female ACS age standardised separations per 100,000 ............................................ 71

Table A.7: Actual and projected ACS separation rates for males ............................................... 74

Table A.8: Actual and projected ACS separation rates for females ............................................ 75

Figures

Figure 1.1 : Defining ACS over time ............................................................................................ 13

Figure 6.1 : A model of care for rehabilitation ............................................................................ 63

Figure 6.2 : Signalling pathways that activate platelets ............................................................. 67


Glossary

ABS Australian Bureau of Statistics

ACE Angiotensin-converting enzyme

ACS Acute coronary syndrome

AIHW Australian Institute of Health and Welfare

AMI Acute myocardial infarction

BMI Body Mass Index

CHD Coronary heart disease

CRA Comparative risk assessment

CVD Cardiovascular disease

DALY Disability adjusted life year

DBP Diastolic blood pressure

DoFD Department of Finance and Deregulation

DoHWA Department of Health Western Australia

DWL Deadweight loss

ECG Electrocardiogram

ESC-ACC European Society of Cardiology and the American College of Cardiology

EMS Emergency medical services

GBD Global Burden of Disease

IHD Ischemic heart disease

LLA Lipid-lowering agents

NHMRC National Health and Medical Research Council

NSTEACS Non-ST-segment elevation acute coronary syndrome

NSTEMI Non-ST-segment elevation myocardial infarction

PBAC Pharmaceutical Benefits Advisory Committee

PCI Percutaneous coronary intervention

QALY Quality-adjusted life year

SBP Systolic blood pressure

STEMI ST-segment elevation infarction

TRA Thrombin receptor antagonist

VSLY Value of a statistical life year

YLD Years of health life lost due to disability

YLL Years of healthy life lost due to premature death


Definitions

Acute coronary syndrome An umbrella term for conditions resulting from sudden insufficient blood supply to the heart. These include chest pain (unstable angina) and heart attack (AMI).

Acute myocardial infarction A sudden insufficient blood supply to the heart muscle (myocardium) occurring because of blocked or narrowed arteries. Shown on an ECG as a Non-ST-segment elevation myocardial infarction (NSTEMI) or a ST-segment elevation myocardial infarction. A heart attack.

Angina pectoris The medical term for chest pain that is due to coronary heart disease. It is a symptom of acute myocardial infarction. Described as uncomfortable pressure in the centre of the chest. Manifested as stable angina or unstable angina.

Burden of disease The impact of a disease or condition on the health and mobility of an individual.

Deadweight loss Inefficiencies created in the economy through distortions created by increased taxes to fund public health care.

Direct health care system costs Public and private costs directly associated with the provision of health care.

Event The occurrence of unstable angina or AMI. It can include a separation, death, or separation and death.

Health capital The stock of human capital that produces health. This can depreciate with age and ill health, or increase with investment (such as exercise).

Indirect costs Costs to the economy associated with flow on effects from reduced health and mobility, such as productivity loss and informal care costs.

Myocardial infarction Reduced blood flow causing damage to the heart muscle. Heart attack.

Separation An admitted patient episode of care. A period of hospitalization.

Separation rate The number of separations compared to the number of individuals within the relevant population.

Stable angina

Thrombus

Unstable angina

Chest pain and discomfort that is instigated by stress or exercise, most commonly caused when the heart is working hard, but not getting enough blood and oxygen. A blood clot that forms inside a blood vessel or cavity of the heart. Reduced blood flow to the heart muscle causing severe chest pain but without damage to the heart muscle. It is usually unexpected and usually occurs at rest.


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1 The epidemiology of ACS in Australia

Coronary heart disease (CHD) (also known as ischemic heart disease) is one of the major causes of morbidity in Australia and the largest single cause of death, accounting for around 23,570 deaths in 2005 (AIHW 2007; 2008). It is associated with significant cost to the health care system, individuals, and society in general (Access Economics 2005).

Acute coronary syndrome (ACS) is a sub-group of CHD and is associated with unstable angina and acute myocardial infarction (AMI). It includes clinical presentations that span ST-segment-elevation1 myocardial infarction to an accelerated pattern of angina without evidence of necrotic damage to the heart muscle (myonecrosis) (Chew et al 2005).

The common underlying cause of ACS is a build up of cholesterol plaque on the inside of the arteries of the heart muscle (known as atherosclerosis), causing the muscle cells to enlarge and form a hard cover over the area. This narrows the artery, reducing blood supply (and hence oxygen) to the heart. Under normal conditions blood flow may still be adequate but may be insufficient when an elevated blood flow is required (for example, through exercise). This is known as stable angina and is not considered part of ACS.

However, if the plaque ruptures from the artery wall it can cause a blood clot within the artery, significantly reducing blood flow or completely blocking blood flow to the heart muscle. This can cause the sudden onset of angina (unstable angina) leading to severe chest pain and potential damage to the heart muscle (acute myocardial infarction). Death can occur if blood flow is not quickly restored to the heart muscle through the use of drugs or catheter procedures.

1.1 Definition of ACS ACS is defined across a range of acute myocardial ischemic states. It encompasses unstable angina, non-ST-segment elevation myocardial infarction (NSTEMI), and ST-segment elevation myocardial infarction (STEMI) (Grech and Ramsdale 2003a).

Figure 1.1 shows the definition of ACS components over time. An initial electrocardiogram (ECG) is conducted to determine whether ST-segment-elevation is present. If at a hospital, myocardial biomarker levels will also be tested. The ECG results and the myocardial biomarker levels will determine the diagnosis and the treatment path. If STEMI is present on the ECG, patients are diagnosed as having an AMI (heart attack) requiring urgent reperfusion. If ST elevation is not present, then patients may be diagnosed as having a NSTEMI (if biomarkers are elevated) or unstable angina (if biomarkers are not elevated).

1 Recording of electrical activity of the heart over time using an electrocardiograph


13

Figure 1.1: Defining ACS over time

Source: Aroney et al (2006)

For the purposes of this study, ACS is defined as patients that are diagnosed with unstable angina and AMI. Referring to the World Health Organisation (WHO) ICD-10 codes, ACS incorporates I20.0 for unstable angina (a sub-set of angina pectoris) and all sub-sets within I21 (WHO 2007). These are outlined in more detail in Table 1.1.


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Table 1.1: Definition of ACS used in this study

ICD-10 Group Sub-group

I20.0 Unstable angina Angina:

Crescendo

De novo effort

Worsening effort

Intermediate coronary syndrome

Preinfarction syndrome

I21.0 Acute transmural myocardial infarction of anterior wall

Transmural infarction (acute) (of):

Anterior (wall) NOS

Anteroapical

Anterolateral

Anteroseptal

I21.1 Acute transmural myocardial infarction of inferior wall


Diaphragmatic wall

Inferior (wall) NOS

Inferolateral

I21.2 Acute transmural myocardial infarction of other sites


Apical-lateral

Basal-lateral

High lateral

Lateral (wall) NOS

Posterior (true)

Posterobasal

Posterolateral

Posteroseptal

Septal NOS

I21.3 Acute transmural myocardial infarction of unspecified site

Transmural myocardial infarction NOS

I21.4 Acute subendocardial myocardial infarction

Nontransmural myocardial infarction NOS

I21.9 Acute myocardial infarction, unspecified

Myocardial infarction (acute) NOS

Notes: (a) Includes: myocardial infarction specified as acute or with a stated duration of 28 days or less from onset. Excludes: certain complications following AMI: I25.2, I25.8, I22-I24.1.

Source: WHO (2007)

1.2 Development of ACS ACS begins with a fracture in the protective fibrous cap of an atheromatous plaque (Libby 2001). When these plaques fissure or rupture and core constituents such as lipid, smooth muscle and foam cells are exposed, it leads to the local generation of thrombin and deposition of fibrin (Grech and Ramsdale 2003a). This promotes platelet aggregation and adhesion and the formation of intracoronary thrombus (Grech and Ramsdale 2003b). Downstream


15

embolisation from friable coronary thrombus may occur, leading to focal cell necrosis and the release of cardiac troponins (Heeschen et al 1999).

STEMI usually occurs when thrombus forms on a ruptured atheromatous plaque and blocks an epicardial coronary artery. Patient survival depends on several factors, the most important being restoration of blood flow, the time taken to achieve this, and the sustained patency of the affected artery (Grech and Ramsdale 2003b). NSTEMI is a form of myocardial infarction and these types of patients differ from STEMI only through the absence of ST elevation on the presenting ECG.

Although there is no universally accepted definition of unstable angina, it has been described as a clinical syndrome between stable angina and AMI (Grech and Ramsdale 2003a). Unstable angina can be recognised by ischemic-type chest pain that is more frequent, severe or prolonged than the patient’s usual angina symptoms, occurs at rest or minimal exertion, or is difficult to control with drugs. Recent onset angina is also classified as unstable (Maynard et al 2000).

1.3 Risk factors and co-morbidity associated with ACS Most known risk factors of ACS can be reduced by specific preventative methods such as pharmacotherapy and lifestyle changes (Patel and Adams 2008). These include smoking, high blood cholesterol, physical inactivity, diabetes, high blood pressure, being overweight or obese, and depression and social isolation (Heart Foundation 2009). However, there are also some risk factors of ACS that cannot be reduced, namely age, gender (being male) and a family history of coronary heart disease (Heart Foundation 2009).

As part of the Global Burden of Disease (GBD) Study, the World Health Organization (WHO) developed a method for ‘risk quantification’ to assess the health implications of certain risk exposures and provide a degree of conceptual and methodological consistency and comparability across risk factors (Ezzati et al 2004). Using this methodology, which was established as part of the Comparative Risk Assessment (CRA) study, Vos and Begg (2007) determined that seven risk factors explain 81.5% of CHD deaths and 85.2% of CHD disability adjusted life years (DALYs).2 Although their study did not specifically focus on ACS, unstable angina and AMI make up around 57% of Australian separations associated with CHD.3 As such, risk factors associated with CHD outlined by Vos and Begg (2007) can be used as a good proxy for ACS.

Table 1.2 provides the prevalence distributions of the seven modifiable risk factors recognised by the WHO’s CRA study as having an impact on the prevalence of CHD. Blood pressure, cholesterol, Body Mass Index (BMI) and fruit and vegetable intake are reported at their mean levels (and standard deviations) in the Australian population. Physical inactivity, tobacco and alcohol are provided as the percentage of the Australian population that falls into each category.

2 A DALY is a summary measure of health developed as the measurement unit to quantify fatal and non-fatal health

outcomes, labelled the burden of disease and injury, on populations around the world for the Global Burden of Disease Study (Murray and Lopez, 1996). DALY weights are measured on a scale of zero to one, where a zero represented a year of perfect health and one represented death. Other health states associated with specific conditions are attributed values between zero and one. For example, a DALY weight of 0.238 for unstable angina means a patient who has unstable angina has lost 23.8% of their total health.

3 Derived from http://d01.aihw.gov.au/cognos/cgi-bin/ppdscgi.exe?DC=Q&E=/ahs/pdx0607, accessed 03 April 2009


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Table 1.2: Prevalence distributions for seven lifestyle risk factors by age and sex 2003

15-29 30-44 45-59 60-69 70-79 80+

M F M F M F M F M F M F

Blood Pressure (mmHg)

Mean - - 124 115 131 126 140 138 148 146 154 150

SD - - 11 12 16 17 17 19 19 22 19 21

Cholesterol (mmol/L)

Mean - - 5.5 5.2 5.8 5.8 5.6 6.0 5.6 6.1 5.3 5.9

SD - - 1.0 1.0 1.1 1.1 0.9 0.9 0.9 1.0 1.0 1.0

BMI (kg/m

2)

Mean - - 26.8 25.4 27.5 27.2 27.2 28.5 27.1 27.0 25.8 24.9

SD - - 4.1 5.4 4.0 5.7 3.7 5.8 3.8 5.2 3.5 4.5

Fruit and vegetable

intake (g/day)

Mean 445 484 452 506 496 569 538 602 538 577 538 577

SD 241 237 235 228 245 240 230 234 219 217 219 217

Physical inactivity

(% population)

Vig 10 4 3 2 3 1 1 1 1 0 0 0

Mod 47 37 37 32 37 35 41 38 44 27 30 17

Insuff 23 35 29 38 29 33 26 28 22 28 21 24

Inact 20 25 31 28 32 30 33 33 33 45 49 59

Tobacco (% population)

Smoker - - 31 25 23 18 16 12 9 9 7 2

Non- smoker

- - 69 75 77 82 84 88 91 91 93 98

Alcohol (% population)

Abstain 38 59 36 59 33 59 47 68 52 76 61 73

Low 50 34 50 31 53 32 41 24 41 19 37 21

Hazard 6 5 6 6 7 7 7 7 4 4 1 1

Harmful 6 2 7 3 7 2 5 1 3 1 1 4

Note: Vig = Vigorous, Mod = Moderate, Insuff = Insufficient, Inact = Inactive, Hazard = Hazardous Source: Vos and Begg (2007)

The relative impact of each of the seven risk factors on CHD deaths and DALYs are illustrated in Chart 1.1 and Chart 1.2 respectively.4 Blood pressure and cholesterol levels have the greatest influence on the number of deaths attributable to CHD, and alcohol and tobacco have the lowest. In contrast, cholesterol levels and blood pressure also have the most significant impact on DALYs, but tobacco and low fruit and vegetable intake have the smallest effect.

4 The actual percentages are reported in the sections below.


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Chart 1.1: Share of CHD deaths by risk factors 2003

Source: Vos and Begg (2007) and Access Economics calculations

Chart 1.2: Share of CHD DALYs by risk factors 2003

Source: Vos and Begg (2007) and Access Economics calculations

Similar findings on the contribution of risk factors to CHD morbidity and mortality presented in Vos and Begg (2007) have been found throughout the world. In a study on potentially modifiable risk factors associated with AMI in 52 countries (including developed and less developed countries), Yusuf et al (2004) found that tobacco consumption and high cholesterol were the two strongest risk factors, followed by psychosocial factors, abdominal obesity, history of hypertension, and history of diabetes. Daily consumption of fruit and vegetables, moderate to strenuous exercise and consumption of alcohol more than three times per week reduced the risk of AMI. The odds ratio associated with exposure to multiple risk factors and

Blood Pressure

Cholesterol

BMI

Low fruit and vegetable

Physical inactivity

Tobacco

Alcohol

Blood Pressure

Cholesterol

BMI

Low fruit and vegetable

Physical inactivity

Tobacco

Alcohol


18

the reduction in risk associated with healthy activities from the study are shown in Chart 1.3 and Chart 1.4 respectively.

Chart 1.3: Risk of AMI associated with exposure to multiple risk factors

Note: Smk = Smoking, DM = diabetes mellitus, HTN = hypertension, Obes = Abdominal obesity, PS = Psychosocial, RF = Risk factors. The odds ratios are based on current vs never smoking, top vs lowest tertile for abdominal obesity, and top vs lowest quintile for ApoB/ApoA1. Source: Yousef et al (2004)

Chart 1.4: Reduced risk of AMI associated with healthy behaviour

Note: Smk = Smoking, Fr/vg = fruits and vegetables, Exer = Exercise, Alc = Alcohol. Odds ratios are adjusted for all risk factors Source: Yousef et al (2004)


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1.3.2 Tobacco consumption

Tobacco consumption, particularly the human carcinogens and other toxic properties inhaled through cigarette smoking, is causally related to an increased risk in mortality from many medical conditions, including CHD (Ezzati and Lopez 2004). This link has also been established in reverse, with the risk of AMI and death from CHD decreasing by half one year after quitting, and, after several years, approaching that of non-smokers (Patel and Adams 2008). Vos and Begg (2007) estimated that tobacco consumption accounts for 1.5% of CHD deaths and 1.2% of CHD DALYs in Australia.

The smoking rate for the Australian population has been steadily declining within the last 50 years. More recently, between 1985 and 2007 the prevalence of daily smoking declined by around 15% and 11% for males and females respectively (AIHW 2008b). Trends in daily smoking for those aged 14 years and over are shown in Chart 1.5. In 2006, Australia had the second lowest prevalence rate of smoking amongst OECD (Organisation for Economic Cooperation and Development) countries at 16.8%, the lowest being Sweden (AIHW 2008b).

Chart 1.5: Trends in daily smoking for those aged 14 years and over

Source: AIHW (2008b)

1.3.3 High cholesterol

Cholesterol is a fat-like substance produced by the body which is found in the blood stream and all other parts of the bodies including organs and nerve fibres. Most cholesterol in the body is made by the liver from a variety of foods, but especially from saturated fats. The main factors that can influence an individual’s level of cholesterol include a diet high in saturated fat content, heredity, and various metabolic conditions such as type II diabetes (Lawes et al 20024b).

Cholesterol is thought to accelerate atherosclerosis, and thus influence CHD. However, the exact process remains uncertain. Clear and consistent positive associations between CHD and cholesterol level have been observed in cohort studies, and clinical trials of cholesterol lowering treatments have provided evidence of reversibility (Lawes et al 2004b).


20

Cholesterol is defined as total serum cholesterol expressed in millimoles per litre of blood (mmol/L). Vos and Begg (2007) estimate that high cholesterol accounts for 10.1% of CHD deaths and 5.3% of CHD DALYs in Australia. Average blood cholesterol levels of adults aged between 25 and 64 years were relatively unchanged between 1980 and 2000 (AIHW 2008b).

1.3.4 Body mass index

The body mass index (BMI) provides a general relationship between weight and health (James et al 2004). Excessive body-weight gain results in abnormalities in blood lipids, leading to an increased risk of developing CHD. In particular, the distribution of body fat appears to be an important determinant of the risk of coronary disease and death as patients with abdominal obesity experience the greatest risk (Krauss and Winston 1998). Vos and Begg (2007) estimated that elevated BMI accounted for 3.7% of CHD deaths and 2.5% of CHD DALYs in 2003.

The prevalence of overweight and obese people in Australia continues to increase. In 2004-05 around 2.5 million adults were obese and a further 4.9 million were estimated to be overweight but not obese (AIHW 2008a). This means around 7.4 million people were estimated to have been above the BMI associated with healthy weight.

Trends in overweight and obesity prevalence between 1995 and 2004-05 are shown in Chart 1.6. In nearly every age bracket there has been a steady increase in the prevalence of overweight and obese people in Australia.5 A recent study on adults attending general practice shows that the prevalence of overweight and obese people in Australia has increased from 51% in 1998-99 to 58.5% in 2006-07 (AIHW 2008b).

Chart 1.6: Prevalence of overweight and obese people in Australia

Source: AIHW (2008a)

5 Prevalence of overweight and obese people aged between 65 and 74 years decreased slightly between 2001 and

2004-05.


21

1.3.5 Hypertension

It is generally accepted that blood pressure plays a significant role in accelerating atherosclerosis of the blood vessels and thereby increasing the risk of cardiovascular disease (Lawes et al 2004a). A variety of prospective cohort studies and overviews have demonstrated a strong, continuous temporal association between blood pressure and CHD (APCSC 2003; MacMahon and Rodgers 1993).

The standard unit for measuring blood pressure is mmHg and each 10mmHg below-usual SBP is associated with a 26% (95% confidence interval of 24-29%) lower risk of CHD (Lawes et al 2004a). According to Vos and Begg (2007), high blood pressure accounts for 10.7% of CHD deaths and 4.8% of CHD DALYs in Australia when analysed independently from the other risk factors.

In recent years blood pressure amongst Australians has been trending down. AIHW (2008b) notes that the prevalence of high blood pressure in males and females aged between 25 and 64 years has more than halved. This is shown in Chart 1.7.

Chart 1.7: Trend in blood pressure amongst Australians aged 25 to 64


1.3.6 Diabetes

Diabetes mellitus is a chronic metabolic disease resulting from reduced levels of insulin in the blood, or through ineffective insulin. The consequence is a high level of glucose in the blood that can lead to a number of conditions, including CHD.

People with diabetes are much more likely to have disability from cardiovascular disease than those without diabetes (Franklin et al 2004). According to Vos and Begg (2007), diabetes accounts for around 0.6% of the disability associated with CHD and 3.6% of the years of healthy life lost. Furthermore, around 2.1% of the total burden of disease associated with CHD was attributed to diabetes.


22

The prevalence of diabetes in Australia has increased significantly in the last 20 years. Based on National Health Survey, prevalence has increased from around 1.3% of the population in 1989-90 to 3.4% in 2004-05 (AIHW 2008b). This equates to around 700,000 Australians with diabetes in 2004-05. Given the trends in the number of people with diabetes and the growth in the highest risk age bracket (65-74 years) due to demographic ageing, prevalence of diabetes could increase significantly in the future, with subsequent impacts on the incidence of ACS. Trends in the prevalence of diabetes are shown in Chart 1.8.

Chart 1.8: Trends in diabetes within Australia


1.3.7 Alcohol

Alcohol consumption is linked to long-term biological and social consequences through three outcomes: intoxication, dependence and direct biochemical effects. The direct biochemical effects can influence IHD in both a beneficial and harmful way. Moderate alcohol consumption reduces plaque deposits in arteries, promotes blood clot dissolution and protects against blood clot formation (Zakhari 1997). On the other hand, alcohol increases the risk of high blood pressure (Apte et al 1997) and hormonal disturbances (Emanuele and Emanuele 1997). Consequently, when estimating the burden of alcohol consumption, the overall deaths attributable to alcohol is an underestimation of the true relationship between alcohol consumption and IHD (Rehm et al 2004). Vos and Begg estimated that the net impact of alcohol consumption accounts for 0.8% of CHD deaths and 2.3% of CHD DALYs.

1.3.8 Fruit and vegetable intake

Studies have found that fruit and vegetables provide a protective effect against ischemic heart disease (IHD) (Law and Morris 1998; Ness and Powles 1997). In particular, numerous studies have consistently shown that individuals who eat more fruits and vegetables have a reduced risk of AMI (Rimm et al 1996).

The mean dietary intake of fruit and vegetables (excluding potatoes) is estimated to be 600g/day in adults, 480g/day in children aged 5 to 14 years, and 330g/day in children aged 0 to


23

4 years (Lock et al 2004). Vos and Begg (2007) estimated that low fruit and vegetable consumption accounts for 2.4% of CHD deaths and 1.4% of CHD DALYs in Australia.

1.3.9 Physical activity

The apparent protective effect of being more active has been extensively documented with a significant amount of literature quantifying and qualifying the role of physical inactivity as a risk factor of CHD (Bull et al 2004). There is evidence of a strong inverse correlation of leisure time activity and energy expenditure, habitual exercise and fitness with risk of coronary disease and death (Patel and Adams 2008). The effect appears to be proportional to energy expenditure; the greater the degree of physical activity the lower the risk of coronary events. Vos and Begg (2007) estimated that physical inactivity accounts for 6.6% of CHD deaths and 3.4% of CHD DALYs in Australia.

Data from the National Health Surveys for 1995, 2001, and 2004-05 show there has been little change in the level of physical activity in the Australian population. The proportion of adults (18 years and over) that undertook less than 100 minutes of exercise in the two weeks prior to the surveys has fluctuated between 30% and 35% (AIHW 2008b).

1.3.10 Depression

Depression has been recognised as a common co-morbidity among cardiac patients and an independent predictor of adverse outcomes (Amin et al 2008; Reddy et al 2008). Approximately 20% of patients with a recent ACS have major depression, and almost 20% have minor depression (Carney and Freedland 2008). Numerous studies have documented that depression in patients with ACS is associated with a higher incidence of mortality, recurrent cardiovascular events, and healthcare utilisation (Rozanski et al 2005). Parker et al (2008) determined that only depressive episodes that commenced after an ACS admission were associated with a poorer cardiovascular outcome.

Amin et al (2008) found that reduced levels of omega-3 fatty acids in blood cell membranes, an emerging risk factor for both ACS and depression, could help explain the relationship between depression and adverse ACS outcomes. Furthermore, other studies have found psychobiological processes to underlie the emotional triggering of ACS (Steptoe and Brydon 2009). Patients with advanced atherosclerosis may be triggered into ACS by acute anger, stress and depression. Vos and Begg estimated that CHD accounts for 3.3% of DALYs attributed to depression (Vos and Begg 2007).

1.4 Projected number of ACS events in Australia There are two primary Australian data sources on ACS treatment that were available. The AIHW provides an estimate of the number of separations by event type, gender, and 10 year age brackets, with the latest data being 2006-07 (AIHW 2009). However, there is a possibility that this data may over estimate the real number of separations because it does not adjust for transfers and readmissions related to the same event.

The second data source is based on population-based linkage of health records in the Perth Statistical Division, Western Australia. The linked data is created by determining connections between core Department of Health Western Australia (DoHWA) data collections and other administrative data sources and research collections, based on probabilistic linkage created through the use of similar demographic information(for example, name, sex, date of birth and address). The data collections linked for the purposes of this study are hospital admissions,


24

emergency presentations, and death records associated with the Registry of Births, Deaths, and Marriages.

ACS separation rates per 100,000 people based on 28 day episodes within the Perth Statistical Division are shown in Table 1.3. A breakdown into AMI and unstable angina is shown in Appendix A. The data are derived from hospital admissions regarding diagnoses for AMI or unstable angina in any diagnostic field and includes fatal and non-fatal cases. They relate to residents of Perth aged 35 to 79 years and do not necessarily include persons admitted to hospitals in Perth.

Table 1.3: ACS age standardised separations per 100,000 in the Perth Statistical Division

1998 1999 2000 2001 2002 2003 2004

Male

35-39 94.29 59.39 70.96 81.28 109.19 67.63 64.87

40-44 213.83 193.86 170.49 154.72 179.33 161.42 146.44

45-49 397.95 365.98 345.38 360.5 357.51 367.59 376.70

50-54 659.14 568.43 578.46 593.81 590.75 558.43 617.38

55-59 978.60 831.56 935.90 966.53 881.23 737.16 746.52

60-64 1,631.62 1,455.51 1,263.72 1,130.41 1,244.37 1,153.36 1,133.31

65-69 2,062.73 1,888.57 2,126.07 1,865.43 1,739.60 1,588.87 1,569.30

70-74 2,932.90 2,545.76 2,589.75 2,494.91 2,692.13 2,271.31 2,160.06

75-79 3,536.54 3,466.43 3,349.81 3,356.41 3,208.88 3,164.05 3,007.81

Female

35-39 26.03 14.72 18.53 20.19 20.37 20.56 37.16

40-44 48.99 33.60 40.62 61.90 30.51 29.90 55.73

45-49 98.64 80.51 78.88 87.05 80.60 89.08 109.22

50-54 204.43 203.58 200.49 184.81 169.6 166.31 160.67

55-59 350.18 339.95 342.74 280.74 258.69 255.16 257.21

60-64 565.95 536.99 536.62 482.21 436.62 447.22 444.23

65-69 1,015.39 1,024.31 866.52 812.83 749.69 736.96 643.85

70-74 1,406.34 1,584.70 1,476.78 1,231.23 1,304.21 1,212.77 1,225.07

75-79 2,441.32 2,088.66 2,099.46 2,216.32 2,186.73 2,040.49 1,891.28

Source: Emeritus Professor Michael Hobbs, pers. com. 07 May 2009

As patient records are linked, the Western Australian data linkage information has the capacity to avoid the inflationary effects of transfers and readmissions as it allows a patient to be followed within the hospital system. To provide an estimate of the number of separations within Australia, separation rates for AMI and unstable angina derived from the linked data were applied to the Australian population by age bracket and gender. These were then compared to the separation data supplied by AIHW.

Although the data accounts for re-admissions within a 28 day period there are some limitations in their use for this study. As the most recent data are for 2004, and there is a downward trend apparent in the data between 1998 and 2004, using 2004 data is likely to overestimate the number of separations for 2009. In order to adjust for possible over


25

estimation, linear trends were projected to 2009 (by age group, condition, and gender) to estimate a more recent measure of separation rates.

Actual and projected age standardised separation rates for AMI, unstable angina and ACS for age groups 35 to 79 years are shown in Chart 1.9, Chart 1.10 and Chart 1.11 respectively. In summary:

■ AMI in males has decreased from 425 separations per 100,000 in 1998 to 362 separations per 100,000 in 2004. It is projected that AMI in males will be approximately 326 separations per 100,000 in 2009.

■ AMI in females has decreased from 164 separations per 100,000 in 1998 to 147 separations per 100,000 in 2004. It is projected that rates for AMI in females will be approximately 145 separations per 100,000 in 2009.

■ Unstable angina in males has decreased from 518 separations per 100,000 in 1998 to 382 separations per 100,000 in 2004. It is projected that unstable angina in males will be approximately 275 separations per 100,000 in 2009.

■ Unstable angina in females has decreased from 252 separations per 100,000 in 1998 to 184 per 100,000 in 2004. It is projected that unstable angina in females will be approximately 109 separations per 100,000 in 2009.

■ ACS in males has decreased from 943 separations per 100,000 in 1998 to 744 per 100,000 in 2004. It is projected that ACS in males will be approximately 601 separations per 100,000 in 2009.

■ ACS in females has decreased from 416 separations per 100,000 in 1998 to 331 separations per 100,000 in 2004. It is projected that ACS in males will be approximately 254 separations per 100,000 in 2009.

The faster decline in unstable angina compared to AMI is consistent with the hypothesis purported by Sanfilippo et al (2008) that the uptake of troponin testing between 1998 and 2004 has increased diagnosis of AMI at the expense of unstable angina. According to Emeritus Professor Michael Hobbs (pers. comm. 06 May 2009) it is likely that this trend will continue. However, there is evidence that separation rates for ACS are still declining (as shown in Chart 1.11), which is consistent with the long term decline in 28 day case fatality following AMI (Sanfilippo et al 2008) and CHD (AIHW 2006).

The declining trend in age standardised separation rates for ACS is consistent with AIHW findings that separation rates for CHD have been declining since its peak in the late 1960s (AIHW 2009a). Specifically, AIHW (2009a) showed that CHD rates were 589.2 separations per 100,000 and 304.0 separations per 100,000 for males and females respectively in 1968, but had declined significantly to 132.6 and 76.6 in 2006.


26

Chart 1.9: Actual and projected age standardised separation rates for AMI

Note: Based on age groups between 35 and 79 years old in Perth Statistical Division, Western Australia Source: Emeritus Professor Michael Hobbs, pers. comm. 07 May 2009 and Access Economics calculations

Chart 1.10: Actual and projected age standardised separation rates for unstable angina

Note: Based on age groups between 35and 79 years in Perth Statistical Division, Western Australia Source: Emeritus Professor Michael Hobbs, pers. comm. 07 May 2009 and Access Economics calculations

0

100

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1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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Actual rates Projected rates

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1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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Projected ratesActual rates


27

Chart 1.11: Actual and projected age standardised separation rates for ACS

Note: Based on age groups between 35 and 79 years old in Perth Statistical Division, Western Australia Source: Emeritus Professor Michael Hobbs, pers. comm. 07 May 2009 and Access Economics calculations

One other limitation with the WA linked data for this study was that separation rates were available only for those between 35 and 79 years. Although AIHW data suggests that less than one per cent of all separations for ACS are for those aged less than 35 years (AIHW 2009), evidence suggests that the burden of cardiovascular disease falls particularly heavily on those above the age of 80 (Begg et al 2007; Vos and Begg 2007). Consequently leaving these age groups out of the analysis would underestimate the true number of ACS separations in Australia, and underestimate the costs associated with those separations.

In order to capture separation rates for patients over the age of 79, separation rates between 25 and 79 years were fitted with trends (by condition and gender) and then projected to age groups beyond 79 years6. Separation rates and fitted trend lines are shown in Appendix A. Projected separation rates for AMI and unstable angina are shown in Chart 1.12 and projected separation rates for ACS are shown in Chart 1.13. In summary, age standardised separation rates associated with:

■ AMI (males and females) and unstable angina (females) follow an exponential growth curve with separation rates significantly increasing beyond the age of 80 years;

■ unstable angina (males) follow a polynomial curve, increasing with flatter growth (compared to AMI) beyond the age of 80 years;

■ AMI are larger for males compared to females;

■ unstable angina are larger for males between 35 to 94 years, but female rates become larger than males for 95 years and above; and

6 Unfortunately separations recorded by AIHW are truncated at age 85+ so a comparison of projected separation

rates used in this study for those 85+ could not be made.

0

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1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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28

■ ACS is larger for males but the gap between males and females becomes progressively smaller for those aged 80 years and older.

Chart 1.12: Projected age standardised separation rates by condition 2009


Chart 1.13: Projected age standardised separation rates for ACS 2009


0

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AMI (F)

Unstable angina (M)

Unstable angina (F)

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ACS (F)


29

By projecting each age bracket (between 35 and 79) out to 2009 and fitting individual trends for each year (by condition and gender) to estimate separation rates beyond 79 years, separation rates for age 35 to 100+ were projected for each year from 1998 to 2009. These are shown in Appendix A.

As the projected separation rates are based on rates associated with residents in the Perth Statistical Division they do not pick up differences in rates between Indigenous and non-Indigenous Australians. According to Australia’s Health (AIHW 2008a), Aboriginal and Torres Strait Islander people generally suffer from poorer health outcomes than non-Indigenous Australians. Evidence shows that Indigenous Australians are three times more likely to have a major coronary event compared to non-Indigenous Australians across all age groups less than 75 years (Mathur et al 2006). Mortality rates for Indigenous Australians from a major coronary event are 1.5 times higher than for non-Indigenous Australians (Mathur et al 2006). Furthermore, Indigenous Australians have higher rates of chronic kidney disease, which contributes to ACS incidence and can lead to adverse outcomes after an event.

Overall, cardiovascular disease mortality rates amongst Indigenous Australians have been increasing since 1977, even though this increase has been slower since the 1990s (Thomas et al 2006). In comparison, mortality rates for all Australians have been falling significantly. These are consistent with the results found in You et al (2009) for Indigenous Australians in Northern Territory.

The discrepancy between the prevalence and mortality rates of ACS between Indigenous and non-Indigenous populations is due, in part, to the higher reported prevalence of factors that increase the risk of coronary heart disease (CHD) for Indigenous Australians. In 2004-05, Indigenous Australians were more likely to be overweight or obese, be physically inactive, and have diabetes and high blood pressure. Indigenous Australians were also twice as likely to be current smokers and had higher rates of consuming alcohol at high-risk levels and using illicit substances compared to non-Indigenous Australians. These factors also contribute to poor survival after an event.

Given that Indigenous Australians have higher rates of CHD and die from this condition at more than twice the rate of non-Indigenous Australians, it is important to ensure they have equal access to optimal care. However, data shows that the rate of cardiac angiography and revascularisation (including PCI and CABG) is 40% lower for Indigenous Australians (Mathur et al 2006). This is consistent with the results in Coory and Walsh (2005), who found rates of PCI were significantly lower by 39% compared to non-Indigenous Australians. Furthermore, Indigenous Australians tend to have relatively poor access to rehabilitation and secondary prevention after an ACS event, which is likely to be playing a role in the worse survival outcomes.

It is clear that the burden of disease of CHD is even greater for the Indigenous population. The variation in the epidemiology and treatment of Indigenous patients should be included in any future research plan associated with ACS in Australia.

1.4.2 Projected number of separations

To determine the number of annual separations associated with AMI, unstable angina and ACS, projected age standardised separation rates for 2009 were applied to projected


30

population estimates derived from the Access Economics’ Demographic mode (AE-DEM).7 These are shown in Chart 1.14 and are compared to projected number of separations based on data from the National Hospital Morbidity Database (AIHW 2009).

Chart 1.14: Comparison of projected ACS separations in Australia 2009

Source: Emeritus Professor Michael Hobbs, pers. comm. 07 May 2009, AIHW (2009), Access Economics calculations

In total, it is projected that there will be around 79,900 separations associated with ACS in Australia in 2009. Of these, 47,539 are expected to be associated with AMI while 32,452 are expected to be associated with unstable angina.

In comparing the number of separations to AIHW data, projections using WA linked data are smaller, particularly for AMI where projections based on the AIHW dataset is around 19.5% greater. However, this difference is expected as WA linked data reduces the inflationary effect of readmissions and transfers. For unstable angina, where readmission and transfers are less likely, the difference is less pronounced, with AIHW separations being around 12.5% greater8.

The projected number of ACS separations was further broken down into age, gender, and condition and are shown in Chart 1.15, Chart 1.16, Chart 1.17, and Chart 1.18. These are summarised below.

7 AE-DEM is a model containing detailed projections of Australia’s population. Building up from the demographic

‘first principles’ of births, deaths, migration and household formation, the model projects population by age and gender for each State and Territory.

8 According to Emeritus Professor Michael Hobbs (pers. comm. 22 May 2009), Perth may have lower ACS rates than

the national average. Although the World Health Organisation ‘s MONICA study (AIHW 2000) found that AMI rates were much higher for those aged between 35 and 64 in Newcastle (NSW) compared to Perth between 1984 and 1993, it may be the case that Newcastle has a higher rate than the national average.

0

20,000

40,000

60,000

80,000

100,000

AMI Unstable angina Total

Se

pa

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s

Projections using WA linked data

Projections using AIHW data


31

■ Total number of ACS separations associated with males is projected to be 48,820 (61%) in 2009.

AMI is expected to account for 28,596 (59%) while unstable angina is expected to account for 20,224 (41%).

Separations for AMI are expected to peak for males aged between 80 and 84 while the number of separations for unstable angina are expected to peak for males aged between 70 and 74 years.

ACS separations are expected to peak for males aged between 75 and 79.

■ Total number of ACS separations associated with females is projected to be 31,170 (39%) in 2009.

AMI is expected to account for 18,943 (61%) while unstable angina is expected to account for 12,228 (39%).

Separations for AMI and unstable angina are expected to peak for females aged between 85 and 89.

ACS separations are expected to peak for females aged between 85 and 89.

■ Total number of ACS separations is projected to be 79,990.

AMI is expected to account for 47,539 separations (59%) while unstable angina is expected to account for 32,452 separations (41%).

ACS separations are expected to peak for people aged 75 to 79 years.

Chart 1.15: Projected male separations in Australia 2009


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Chart 1.16: Projected female separations in Australia 2009


Chart 1.17: Projected total separations in Australia by condition 2009


0

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33

Chart 1.18: Projected total separations in Australia by gender 2009


State and territory breakdown of separations

State and territory breakdowns of AMI and angina pectoris public separations (unstable angina could not be separated) derived from AIHW Hospital Statistics (AIHW 2008) are presented in Chart 1.19.

Shares generally follow the share of population in Australia for each state and territory. NSW has the greatest share of AMI, accounting for around 35% of all public separations in Australia in 2006-07, although its share of angina pectoris is only 30%. There are small share differences between AMI and angina pectoris for Victoria and Queensland, while shares are virtually the same across conditions for the remaining states and territories.

0

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34

Chart 1.19: Share of AMI and angina pectoris across states and territories 2006-07

Source: AIHW (2008)

1.4.3 Projected number of deaths

Separations associated with ACS can result in two outcomes – recovery or death. Trends in 28 day case fatality for persons aged 35 to 79 years old derived from the WA linked data are shown in Chart 1.20. Between 1980 and 2004 there has been a significant reduction in 28 day case fatality following AMI, falling from around 13.5% and 18.1% for males and females respectively to around 4.7% and 7.1%. Using the linear trends established between 1980 and 2004, projected 28 day case fatalities for 2009 are expected to be 2.6% and 3.6% for males and females respectively.

Although females have lower rates of ACS, they have a higher rate of 28 day case fatality than males, although the gap has narrowed over the last 25 years. According to Emeritus Professor Michael Hobbs (pers. comm. 12 May 2009), males tend to have more sudden deaths before hospitalisation and the risk factors for ACS tend to be different in females, with higher prevalence of both hypertension and diabetes that worsen the prognosis.

The 28 day case fatality presented in Chart 1.20 provides an estimate of the number of deaths resulting from ACS after a person has been admitted to hospital and within 28 days of being admitted. However, there are a significant proportion of people who do not survive an event before they get to the hospital, or do not survive after 28 days. Estimates based on 35 to 79 year olds from the WA linked data suggest around 70% of total deaths from CHD are out of hospital (Emeritus Professor Michael Hobbs, pers. comm. 12 May 2009). Chew et al (2008) found that overall mortality associated with ACS was significant up to 12 months, with mortality associated with patients experiencing STEMI, non-STEMI, unstable angina, and stable angina being 8.0%, 10.5%, 3.3%, and 3.7% respectively. Due to the risk of missing out on a significant number of deaths associated with ACS, mortality data from the WA linked database was not used in estimating the number of deaths associated with ACS.

Acute myocardial infarction

35%

27%

18%

8%

8%

1%2% 1%

Angina pectoris

30%

25%

22%

9%

8%

3% 2%1% NSW

Vic

Qld

WA

SA

Tas

ACT

NT


35

Chart 1.20: 28 day case fatality following AMI

Note: Based on age groups between 35 and 79 years old in Perth Statistical Division, Western Australia Source: Sanfilippo et al (2008)

To determine the total number of deaths associated with ACS, data were extracted from the Australian Bureau of Statistics publications titled Causes of Death, Australia. The underlying cause of death was AMI. Although a small number of deaths were recorded for angina pectoris (27 deaths in 2007), the data did not indicate deaths associated with unstable angina so they were not included in the study.

The most recent year for which this data are available is 2007, however only the total number of deaths by gender is reported. To obtain an estimate for 2009, the total number of deaths due to AMI was linearly extrapolated to 2009 for each gender from a time series spanning 2003 to 2007. It was further broken up into age groups by assuming that the share of each age group in the total remains the same as presented in the 2007 data.

Chart 1.21 shows the actual and projected number of deaths following AMI in Australia between 2003 and 2009, while Table 1.4 presents projected deaths for 2009 by gender and age bracket. In 2003 there were around 13,149 deaths, dropping to around 11,332 in 2007. In 2009, it is expected that there will be a total of 9,959 deaths following AMI. Of these, males will account for around 50.4% and females will account for around 49.6%. Compared to the projected number of AMI separations for 2009, this would suggest deaths occurring within 28 days of a separation account for around 24.3% of all deaths associated with AMI.

The decline in deaths and death rates associated with AMI can be attributed to a number of factors. The WHO MONICA project (AIHW 2000) examined trends in AMI in 33 populations in 22 countries between 1984 and 1993. It found that large decreases in AMI rates could be attributed to lifestyle changes, accounting for around 70%. The remainder could be attributed to changes in medical treatment. AIHW (2009a) notes that the decline in rates of CHD can be attributed more recently to improvements in detection, prevention, treatment and rehabilitation care. Emergency medical services for heart attack have improved and increases in specialist ACS care facilities around the country have also contributed to improved survival

0

5

10

15

20

25

30

1980 1984 1988 1992 1996 2000 2004

Ag

e-s

tan

dard

ized

28-d

ay c

ase-f

ata

lity

(%

).

OR(slope) = 1.004

(95% CI: 0.974, 1.034)

OR(slope) = 0.965

(95% CI: 0.941, 0.989)

1980–1988 1989–1997 1998–2004

OR(slope) = 0.921

(95% CI: 0.895, 0.948)

OR(slope) = 0.942

(95% CI: 0.909, 0.976)

OR(slope) = 0.927

(95% CI: 0.880, 0.977)

OR(slope) = 0.924

(95% CI: 0.866, 0.985)

Women

Men


36

probabilities and times. Furthermore, AIHW notes reductions in some risk factors have contributed, for example the prevalence of daily smoking has declined by 45% for males and 42% for females between 1985 and 2007 (AIHW 2008a).

Chart 1.21: Actual and projected deaths following AMI Australia


Table 1.4: Projected deaths following AMI by age bracket Australia 2009

Males Females Total

35–44 years 81 18 99

45–54 years 239 44 283

55–64 years 478 137 615

65–74 years 892 428 1,320

75–84 years 1,905 1,570 3,475

85 years and over 1,427 2,741 4,167

Total 5,022 4,937 9,959


1.5 Impact of demographic ageing It is problematic to project ACS separation rates and total separations into the future as the declines in rates between 1998 and 2004 may not be representative of the long term trends. It is more than likely that separation rates will continue to decrease in the immediate future, but for how long and how much is unknown.

As separation rates decline, it could be expected that further improvements in treatment may be harder to achieve (that is, the marginal benefit of additional investment decreases as total investment increases). Furthermore it is unclear how current trends in the risk factors associated with ACS will impact on separation rates in the future. For example, as shown in Section 1.3, tobacco use is currently decreasing in Australia along with the prevalence of high

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2003 2004 2005 2006 2007 2008 2009

Death

s f

oll

ow

ing

AM

I

Males Females Total Actual Projected


37

blood pressure, while the prevalence of overweight and obese people and the prevalence of diabetes are increasing rapidly.

One undisputable fact is that the Australia population is becoming older and this will put increased pressure on ACS resources. For example, Chart 1.22 shows the larger bulge for age brackets 70 to 74 and above, with the proportion of people in high risk age groups (65+ years) expected to increase from 13.9% to 23.2% between 2009 and 2050.

Chart 1.22: Projected Australian population age structure

Source: ABS (2008) and Access Economics calculations

In order to gauge the pressure demographic ageing will have on the number of separations in Australia, the separation rate was held constant at 2009 levels and total separations were projected based on expected population growth and demographic ageing. Chart 1.23 shows that in 2010 total ACS separations are expected to be around 82,429 but are projected to increase to 246,031 by 2050 (or a 199% increase). Consequently, if the future trend in separation rates starts to flatten, or even starts to increase as diabetes and obesity continue to rise, then the expected impact on direct health care system costs and indirect costs associated with productivity losses is likely to be compounded significantly by the changing underlying demographic structure of the Australian population.

2009

4.02.00.02.04.0

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+

% of total population

2050

4.02.00.02.04.0

0-45-9

10-1415-1920-2425-2930-3435-3940-4445-4950-5455-5960-6465-6970-7475-7980-8485-8990-9495-99100+

% of total population

Female

Male


38

Chart 1.23: Projected ACS separations in Australia

Note: Assumes separation rates for AMI and unstable angina are held constant at projected 2009 levels Source: Access Economics calculations

0

50,000

100,000

150,000

200,000

250,000

300,000

2010 2020 2030 2040 2050

Se

pa

rati

on

s p

er

ye

ar


39

2 Direct health care system costs

Although the treatment of ACS will clearly depend on the severity of the symptoms being experienced, the presence of ACS symptoms should be treated as a medical emergency. All patients will undergo a 12-lead electrocardiogram (ECG) to determine the type and severity of the ACS event. AMI events are likely to require reperfusion, which restores blood flow to the heart and can be achieved through angioplasty or fibrinolysis.

According to ACS Guidelines (NHF and CSANZ 2006), the preferred type of reperfusion is angioplasty, which can only be performed within catheterisation laboratories. However, fibrinolysis is still the most common form of treatment performed for ACS. Post operative care is also an important part of effective ACS management and includes therapies such as aspirin, β-blockers, angiotensin-converting enzyme (ACE) and lipid-lowering agents (LLAs).

The direct health care system costs related to ACS are therefore mostly composed of inpatient and pharmaceutical costs, since in-hospital procedures and drug therapy are the most important and vital part of ACS treatment. Patients also need to consult GPs and specialists in relation to their condition, and require specialised pathology and imaging, such as echocardiography.

2.1 Methodology Estimates of direct health system costs are based on data from the Australian Institute of Health and Welfare (AIHW), provided in a special data request. This data contains allocated expenditures on Ischemic Heart Disease (ICD-10 codes I20–I25) for years 2000-01 and 2004-05. The AIHW take a ‘top down’ approach to estimate expenditures associated with different conditions, where total health system expenditures are first estimated and are then assigned to the relevant conditions, based on the principal diagnosis of the patient. Expenditure estimates are allocated across conditions using data from the hospital establishments collection, hospital morbidity records and Casemix, Medicare, the Pharmaceutical Benefits Scheme (PBS), the Pharmacy Guild Survey, and the BEACH (Bettering the Evaluation and Care of Health) survey of general practice (AIHW 2008b). The data includes costs associated with:

■ hospital inpatients and outpatients;

■ aged care;

■ specialist and primary medical care;

■ pathology and imaging;

■ pharmaceuticals;

■ research; and

■ other professional services.

Due to changes in these categories from 2000-01 to 2004-05, some calculations were performed on the 2004-05 data in order to maintain comparability with the earlier period. Specifically, the 2004-05 data excludes costs related to outpatients; these were calculated assuming that the proportion of outpatient costs to total hospital costs remains as in 2000-01.9

9 Total hospital costs consist of inpatient and outpatient costs.


40

Costs related to other health professional services and aged care were also omitted from the 2004-05 data and a similar procedure was used to estimate these costs.10

An attempt was made to obtain specific expenditure data for Acute Coronary Syndrome (ACS) (ICD-10 codes I20.0 and I21), however the AIHW were only able to supply the total cost for ICD-10 codes I20-I25. Thus, in order to obtain costs relating specifically to ACS, AIHW data on patient days by principal diagnosis was utilised. Specifically, the numbers of patient days for unstable angina (I20.0) and acute myocardial infarctions (I21) were compared to the total number of patient days for all Ischemic Heart Disease categories (I20-I25), in 2000-01 and 2004-05. These proportions were applied to the year-appropriate Ischemic Heart Disease cost statistics and specific costs were calculated for unstable angina and AMI for 2000-01 and 2004-05.11

To estimate ACS costs in 2009, unstable angina and AMI costs were each linearly extrapolated to 2009 based on the trend from 2000-1 to 2004-05. This method encompasses trends in the prevalence of these two conditions and in health inflation. It also assumes that trends observed between 2000-01 and 2004-05 persist for the next four years, to 2009. To estimate the direct health care system costs per separation for AMI and unstable angina in 2009, total direct health care system costs of each condition were divided by the projected number of separations for each condition (as shown in Chapter 1).

There are potential problems with linearly extrapolating costs based on the observed trend. There has been a large increase in the use of catheterisation labs in recent years, which are more expensive, compared to fibrinolysis. This could lead to an underestimation in the rise in costs. However, catheterisation labs have also led to better outcomes, in terms of fewer complications and reduced hospital bed days, which lower costs and could lead us to over-estimate of the cost increase. It is uncertain which effect dominates.

2.2 Direct health care system costs It is projected that direct health care system costs will be around $1.8 billion for 2009. Table 2.1 shows the direct health care system costs of ACS by gender and age group. Costs are substantially higher for males, accounting for around 62% of total costs. Males aged between 65 and 74 account for the largest direct health care system costs associated with ACS. For women, the highest costs are in the 75 to 84 age group. The distribution of costs by age group can be seen in Chart 2.1.

10

A subtotal was calculated for 2000-01, excluding other health professional services and aged care to obtain the shares of these two categories, these shares were then applied to the 2004-05 data and the total cost was increased to include these two categories.

11 Proportions were calculated for years 2000-01 through to 2006-07 for both patient days and separations, in order

to check for consistency; trends in the two measures were consistent.


41

Table 2.1: Projected direct health care system costs by age and gender 2009

Unstable Angina AMI ACS


Males

0-4 0 0 0

5-14 0 0 0

15-24 0 1 1

25-34 2 8 10

35-44 13 31 44

45-54 40 92 132

55-64 100 172 271

65-74 104 191 295

75-84 89 190 279

85+ 16 56 72

Total - Males 364 741 1,104

0-4 0 0 0

5-14 0 0 0

15-24 0 0 0

25-34 2 3 6

35-44 6 11 17

45-54 22 29 51

55-64 40 57 97

65-74 46 86 132

75-84 69 157 226

85+ 27 106 134

Total - Females 213 450 663

All

0-4 0 0 0

5-14 0 0 0

15-24 0 1 1

25-34 4 11 16

35-44 19 42 61

45-54 62 121 183

55-64 140 229 369

65-74 150 277 427

75-84 159 346 505

85+ 43 162 205

Total 577 1,191 1,767

Source: Access Economics calculations based on AIHW special data request


42

Chart 2.1: Distribution of direct health care system costs of ACS 2009

Source: Access Economics based on AIHW special data request

Costs by expenditure type are presented in Table 2.2 and Chart 2.2, Chart 2.3, and Chart 2.4. Inpatients account for by far the largest share of total ACS costs at around 63.4%. Costs associated with pharmaceuticals are the next largest category, representing around 20% of total costs. This is followed by out-of-hospital specialists and outpatients, at 5% and 4% respectively.

Table 2.2: Projected direct health care system costs, by expenditure type 2009

Unstable Angina AMI ACS


Inpatients 361 760 1,120

Outpatients 22 47 69

Aged care 10 20 30

GPs 11 23 34

Imaging & Pathology 10 21 30

Out-of-hospital specialists 30 60 90

Pharmaceuticals 114 219 333

Other professional services 6 13 19

Research 13 29 42

Total 577 1,191 1,767

Source: Access Economics calculations based on AIHW special data request

0

50

100

150

200

250

300

350

25-34 35-44 45-54 55-64 65-74 75-84 85+

$ (

mil

lio

n)

Males Females


43

Chart 2.2: Direct health care system costs of ACS by expenditure type 2009


Chart 2.3: Direct health care system costs of AMI by expenditure type 2009


Inpatients63%

Outpatients4%

Aged care 2%

GPs2%

Imaging & Pathology

2%

Out-of-hospital specialists

5%

Pharmaceuticals19%

Other professional services

1%

Research2%

Total (m)

$1,767.4

Inpatients64%

Outpatients4%

Aged care 2%

GPs2%

Imaging & Pathology

2%


5%

Pharmaceuticals18%


1%

Research2%

Total (m)

$1,190.88


44

Chart 2.4: Direct health care system costs of unstable angina by expenditure type 2009


2.3 Trends in direct health care system costs Table 2.3 shows the trend in patient days associated with ACS has been decreasing since 2000-01. However this trend has not been uniform across conditions. For example, patient days for those diagnosed with unstable angina have decreased from 243,538 in 2000-01 to a projected 111,496 in 2009. However, patient days have been increasing for those diagnosed with AMI, from 234,326 in 2000-01 to a projected 302,798 in 2009. When combined, the two conditions reveal an overall decreasing trend in ACS from 2000-01 to 2009 of around 63,571 or 13%.

The increased trend in patient days for AMI is likely to be partly caused by the change in the definition of AMI. In 2000, an international consensus document was published by the American College of Cardiology and the European Society of Cardiology (ESC-ACC) that revises the definition of AMI. The new definition of AMI confirms troponin as the most appropriate biomarker (Urban et al, 2008) and lowers the troponin threshold for diagnosing AMI. Troponin testing is a more specific and sensitive test for myocardial necrosis, compared to other available biomarkers, and in itself improves the diagnosis of mild cases of AMI (Sanfilippo et al, 2008). Furthermore, the lower troponin threshold would have increased the number of patients diagnosed with AMI and created a subgroup of patients that would have been categorised as unstable angina according to the earlier definition and are now diagnosed as AMI (Urban et al, 2008). This would lead to a decreasing trend in unstable angina patient days and an increasing trend in AMI patient days.

Inpatients62%

Outpatients4%

Aged care 2%

GPs2%

Imaging & Pathology

2%


5%

Pharmaceuticals20%


1%

Research2%

Total (m)

$576.5


45

Table 2.3: Patient days associated with unstable angina and AMI


2000-01 243,538 234,326 477,864

2001-02 222,223 246,650 468,873

2002-03 205,664 258,544 464,208

2003-04 190,687 270,125 460,812

2004-05 175,334 270,756 446,090

2005-06 159,407 276,274 435,681

2006-07 145,650 284,696 430,346

200912 111,496 302,798 414,293

Source: AIHW (2009)

Total projected direct health care system costs associated with ACS are presented in Table 2.4. The cost of AMI is expected to increase by around 140% between 2000-01 and 2009. The increase in the cost of unstable angina over the eight years is projected to be more moderate at around 16%, while the overall cost of ACS is projected to increase by around 77% to around $1.8 billion.

Table 2.4: Trend in direct health care system costs associated with ACS



2000-01 506 497 1,003

2004-05 546 844 1,390

2009 576 1,191 1,767


The decrease in the number of patient days and the increase in total health care costs associated with ACS seem contradictory at first glance. However, the pattern is likely to be caused by several factors. First, since the costs reported by AIHW are nominal, they include health inflation for this period. Thus the same procedures are costing more. AIHW (2009a) estimated that health inflation averaged 3.1% between 1995-96 and 2005-06, which would account for around $277.5 million if applied to the 2000-01 costs up to 2009.

Furthermore, the overall decrease in patient days is likely to be caused by the more frequent use of catheterisation labs, which provide better outcomes and a reduced need to observe patients compared to fibrinolysis, but are also more expensive. Average length of stay in hospital after an AMI event has been reduced significantly as a result of catheterisation labs due to the reduced risk of death associated with the use of stenting with PCI. Moreover, the increased use of drug eluting stents, which are also more costly, has contributed to the observed increase in costs, along with the use of more expensive pharmaceuticals.

2.4 Direct health care system cost per separation The cost per separation for AMI and unstable angina was calculated using the estimated number of separations in 2009 derived from Chapter 1 and the estimated total cost of AMI and

12

Estimate – based on a linear extrapolation using the trend between 2000-01 to 2006-07.


46

unstable angina for 2009. The cost per separation in 2009 is projected to be $25,051 and $17,766 for AMI and unstable angina respectively. These costs are broken up into their components and are presented in Table 2.5.

Table 2.5: Direct health care system costs per separation 2009

Category Unstable Angina AMI

$ $

Inpatients 11,114 15,982

Outpatients 684 984

Aged care 300 422

GPs 339 489

Imaging & Pathology 295 433

Out-of-hospital specialists 914 1,269

Pharmaceuticals 3,526 4,600

Other professional services 191 270

Research 404 602

Total 17,766 25,051



47

3 Indirect costs associated with ACS

This chapter investigates indirect costs that are related to ACS. As they do not relate to the direct health care system costs, these costs are indirectly associated with ACS rather than costs associated with treatment. Indirect costs investigated within this chapter include:

■ productivity losses from reduced labour market participation through lower employment, greater absenteeism, and premature mortality associated with ACS;

■ costs to informal carers from providing care to someone who has experienced an ACS event;

■ private costs associated with rehabilitation; and

■ deadweight loss associated with raising additional tax revenue to publicly fund health care services associated with ACS.

In evaluating indirect costs, it is important to make the economic distinction between real costs and transfer payments. A real cost is incurred when economic resources are used in the production of goods and services, such as land, labour and capital. Using resources in one area of the economy reduces the opportunity to produce goods and services in other areas of the economy. Transfer payments are defined as payments from one economic agent to another that are made without receiving any good or service in return. Rather than payments made for the use of any good or service, they are a transfer of claims over real resources. Some examples of transfer payments include taxes, subsidies, unemployment benefits and pensions. As transfer payments do not represent a real economic cost they have not been presented as an economic cost within this study.

3.1 Productivity losses There are a number of theoretical links between the level of an individual’s health and their labour supply (Grossman 1972). Quite simply, better health outcomes allow an individual to increase their supply of labour and to work more productively. Poor health outcomes are likely to be associated with lower labour supply and lower productivity, thereby imposing a cost on the economy.

The cost of lost labour supply and productivity due to ACS were estimated as the earnings lost as a result of ACS-related mortality and morbidity. In estimating the cost, the human capital approach was used, which assumes that an employee cannot be easily replaced from the pool of the unemployed population, and thus that premature death or absence from work would result in a loss of productivity in the economy. Under the human capital approach, a loss in productivity due to ACS will only equate to a loss in productivity to the economy under fairly strict conditions. These are:

■ the economy is at full employment so any reduction in hours worked due to ACS, or any permanent reduction in labour force participation through early retirement or death, cannot be replaced by employing or increasing hours of other workers; and

■ the income of an individual is proportional to the total value added to production.


48

The first condition will fluctuate over time as the economy moves into, and out of, full employment. A reduction in labour when labour is scarce will have a greater impact on productivity compared to an economy with an abundant labour supply. Although the Australian economy is currently close to full employment it is problematic to determine the scarcity of labour into the future. Given demographic ageing and current immigration and workforce policy, it is reasonable to assume that the long term goal of government is to keep the economy at full employment. This means a temporary or permanent reduction in working hours due to ACS cannot be replaced by another worker. Consequently a loss in productivity due to sight loss is expected to represent a real cost to the Australian economy.

The second condition (income of an individual is proportional to the total value added to production) will occur if there is a perfect labour market such that the marginal benefit from an additional hour of work (the value added) is equal to the marginal cost (the wage). In reality, the labour market is far from perfect for a number of reasons, for example asymmetric information within the market and labour market restrictions imposed by government regulation and natural labour market barriers. In addition, synergy created between labour, capital and land means a reduction in working hours may also impact the productivity of other factors of production. Consequently, the value of productivity from labour is expected to be larger than the wage provided to an individual, so using lost income as a proxy for lost productivity will tend to underestimate the true cost.13

The productivity lost due to premature death associated with ACS was calculated by multiplying the number of deaths that resulted from ACS in 2009, for those aged between 35 and 64, with the residual expected lifetime earnings at the time of death. Specifically, the employment to population ratio for males and females (ABS 2009)14 was applied to estimate the number of employed individuals who died as a result of ACS. Assuming a retirement age of 65, the residual number of years of employment was calculated by using the midpoint age of each age group, while the gender-specific gross yearly wage (ABS 2008a)15 was used to obtain the residual earnings for males and females in each age group. The present value of these future earnings was estimated by assuming a five per cent per annum discount rate (NHMRC 2001).

The productivity losses due to ACS-related morbidity include the lost gross earnings for time taken off work following an ACS event. Estimates of ACS-related morbidity were calculated as the difference between the projected number of separations for ACS and the number of deaths resulting from ACS extrapolated to 2009.

The time taken off work following an ACS event was estimated based on research findings in this area. Specifically, the shortest time off work is assumed to be two months, based on evidence that a return to work sooner than eight weeks after an AMI event is usually not recommended (Kovoor et al 2006). It is also assumed that 80% of patients return to work within 12 months following an ACS event, as reported in Bhattacharyya et al (2006) using UK data. This study also finds that the average time between ACS and the return to work is 3.4 months, while Kovoor et al (2006) report an average time of 2.7 months. Based on these 13

One criticism of the human capital approach is that productivity losses for those outside the labour market (for example, students, homemakers and volunteers) are not included in the estimation of total costs (Liljas 1998).

14 These are 77.9% and 66.6% respectively.

15 The latest available data is for October 2008, when the seasonally adjusted gross years wage was $57,356 for

males and $37,430 for females.


49

findings, the average time to return to work after ACS is taken to be three months in this report. Moreover, is assumed that around 55% of patients had commenced work within six months, a further 12% within seven months and 9% within nine months. The remaining 20% of patients are assumed to retire or become long-term unemployed until their retirement. For the 80% of patients who return to work within a year, the earnings lost are calculated using the appropriate amount of time taken off work and the gender specific average gross yearly wage (ABS 2008a).16

Based on Zhang et al (2009), it is assumed that the impact of an ACS event on the labour force participation decision would be different between younger and older individuals17. Consequently, the 20% of patients who are assumed to retire are also assumed to be aged between 50 and 64,18 with the midpoint age of 57. Thus, there is on average eight remaining work years for these patients. Their lost earnings are calculated as the present value of eight years of the gross average yearly wage, with a five per cent discount rate.

Table 3.1 presents the discounted productivity loss due to premature death associated with ACS. It is projected that 997 people still in the workforce will die in 2009 due to AMI, resulting in an expected productivity loss of $287 million.

The productivity loss expected to result from ACS-attributable lost working days is summarised in Table 3.2. It is projected that there will be 21,085 employed persons between the age of 35 and 65 experiencing an ACS related separation in 2009 that does not result in death. The total gross earnings lost by these persons are estimated at around $2.3 billion. The largest share of these productivity losses is attributed to persons who do not return to work following ACS, amounting to around $1.9 billion.

Table 3.1: Productivity loss due to premature death 2009

Deaths Discounted productivity loss

No. $(million)

35–44 years 99 57

45–54 years 283 122

55–64 years 615 107

Total 997 287


16

It is assumed that productivity losses are strictly associated with the ACS event. However there is expected to be considerable co-morbidity among patients that are attributable to common risk factors (such as diabetes, obesity and high blood pressure) and these may also reduce the capacity to work after an event.

17 Zhang et al (2009) also suggests the decision to return to work will also differ between males and females,

although this has not been incorporated within this study.

18 Those >64 years are assumed to have already retired.


50

Table 3.2: Productivity loss due to working days lost 2009

Number of persons Earnings lost

$(million)

2 months 2,108 18

3 months 5,271 69

5 months 4,006 88

7 months 2,530 78

9 months 1,898 75

11 months 1,054 51

Retired 4,217 1,949

Total 21,085 2,327


3.2 Cost of informal care An ACS event not only impacts the individual experiencing the event, it can also impact their family and friends. This is typically through the emotional strain an event places on others, such as anxiety and stress associated with uncertainty surrounding survival. However, it can also impact through lifestyle changes that result from caring activities required immediately after an event.

A range of informal care activities are usually provided to individuals who have experienced an ACS event by partners, other family members, and friends. This is especially the case for an AMI event. Clark et al (2007) notes informal care for those with heart failure is complex, as it not only includes typical care activities but also invisible care that are not necessarily observed within the carers behaviour, or known by the individual receiving care. These ‘invisible’ care activities often relate to ensuring the patient is stable. Informal care activities can therefore include:

■ collecting prescriptions and organising and timing the administration of medication;

■ participating in the decision making process to seek help from a health care professional if symptoms change;

■ basic nursing care, such as washing, dressing, assisting with going to the toilet, cooking and laundry;

■ ad-hoc tasks, shopping, transporting and cleaning activities;

■ surveillance of signs and symptoms associated with an ACS event;

■ monitoring of the patient’s physical and mental wellbeing;

■ delivering a support network for any depression that may result from an event; and

■ assessment of certain activities on the patient’s condition (for example, determining the appropriateness of social engagements or work related activities).

It is difficult to separate the time family and friends spend helping someone as a result of an ACS event and the time when they are simply undertaking activities with the person unrelated to the event. It is even more difficult to estimate the cost of informal care as consideration must also be given to the number of people receiving informal care, the amount of time


51

devoted to informal care per day, the number of days informal care is provided, and the value of time associated with informal care (which depends on whether the informal carer has substituted labour supply or leisure time in providing care).

However, international studies suggest informal care costs comprise a significant cost component in the total cost of CHD and cardiovascular disease (CVD). For example, Allender et al (2008) estimated that informal care costs totalled €9.1 billion in 2006 for CHD in Europe (or approximately 18.3% of total costs), and £1.9 billion in the UK (or approximately 20.6% of total costs). Similarly, Liu et al (2002) estimated that informal care costs associated with CHD in the UK totalled £2.4 billion in 1999 (or approximately 34.3% of total costs).

Unfortunately, Australian or international data relating to the number of hours associated with informal care for ACS is not available. Furthermore, data relating to informal care associated with CHD are scarce. Studies typically apply informal care associated with limiting conditions (for example, Allender et al (2008), however these studies suffer from not being able to incorporate the different amounts of time associated with CHD compared to other chronic conditions.

To estimate the cost of informal care associated with ACS, the opportunity cost method was used. This method measures the value in alternative use of time spent caring, which is typically valued by productivity losses (or value of leisure time) associated with caring. It is based on the assumption that time spent providing informal care could be alternatively used within the paid workforce or in leisure activities. The value of informal care provided by one individual using the opportunity cost method can be represented by:

Value of informal care = tiwi

where ti is the time provided by individual i on providing care and wi is the net market wage rate of individual i (van den Berg et al 2006). For those who provide informal care but are not in paid work (for example, children or those who have retired) the value of providing informal care is the value of the lost opportunity of undertaking leisure time. This can be approximated by the willingness to pay to undertake leisure, or to avoid work. However, the value of leisure time is often proxied by an average age and sex specific wage rate (Brouwer and Koopmanschap 2000; Heitmueller 2007). If the value of non-work is more (less) than the average wage rate, the opportunity cost method will under (over) estimate the value of informal care.

To proxy the number of hours of informal care provided to people who experience an ACS event in Australia, the number of hours of informal care per person diagnosed with CHD in one year in the UK was used (Liu et al 2002).19 Of the 1.46 million people diagnosed with CHD in the UK, there were around 408.4 million hours of informal care provided, which equates to around 279 hours of informal care per person, or approximately 12 days of 24 hour care. This seems plausible given part of rehabilitation after an ACS event is a significant amount of bed rest at home.

Applying the number of informal care hours per separation to the number of projected ACS separations in Australia in 2009 (79,900), it is estimated that around 22.3 million hours of

19

This was based on a UK Department of Health study on informal care (Green 1988), although it is unclear whether the study specifically looked at informal care associated with CHD.


52

informal care will be provided in 2009. Multiplying this by the seasonally average gross hourly wage rate in Australia of $31 (ABS 2008a),20 the total cost associated with informal care is projected to be $691.1 million in 2009.

3.3 Private costs associated with rehabilitation Private costs associated with rehabilitation can include costs incurred for the purchase of devices, special equipment, aids and home modifications that allow patients to function adequately. It was estimated that around 20% of people with CVD have levels of disability which would require the use of aids and modifications (Access Economics, 2005).

The main types of CVD that cause disability are stroke (cerebrovascular disease) and heart failure. Since a stroke can damage a part of the brain and thus impair a range of functions, such as movement, vision and communication, it is reported half of the survivors of stroke are disabled in the longer term. These patients would require a number of aids and modifications to be able to function, and would account for most of the need for aids and modifications by CVD patients. It is not likely that ACS contributes to this need, given the nature of rehabilitation.

However, rehabilitation after an event usually involves some form of light exercise to reduce the risk of a repeat event. Consequently, some patients are likely to purchase sporting equipment, exercise clothes and shoes, and may even join a private gym. Furthermore, there are private costs associated with attending rehabilitation centres (for example, travel costs), and opportunity costs associated with time devoted to rehabilitation.

Although these types of private costs represent a direct cost associated with ACS, there are no data available to estimate them. Furthermore, they are likely to be insignificant compared to direct costs and other indirect costs (such as productivity losses and informal care costs). Consequently they have not been included in this study.

3.4 Deadweight loss associated with public funding of health care Public funding of direct health care system costs related to ACS means that the Australian government must increase tax revenue to achieve a budget neutral position.21 Consequently tax rates such as income tax rates and Goods and Services Tax (GST) must be higher that they would have otherwise been.

Tax and subsidy revenue are not an economic cost but a transfer of payments from one individual to another. It has therefore not been included in this study. However, increasing tax revenue is not frictionless as tax reduces the efficiency with which the economy’s resources are used. For example, an increase in income tax rates will increase the relative price of work compared to leisure and therefore create a disincentive to work. Alternatively an increase in the GST increases the price of goods and services that are taxed, resulting in reduced sales. Consequently there is an associated reduction in consumer and producer surplus, which is known as the deadweight loss, or excess burden, of tax.

20

Calculated by dividing the seasonally adjusted gross weekly wage for full time adult ordinary time earnings in November 2008 ($1,166.50) by 38 hours.

21 This implicitly assumes funds have not been directed from some other area of the health care system.


53

While the costs associated with deadweight loss will depend on the method used to raise additional taxes,22 the social cost will not be zero and should therefore be included as a cost of ACS. The usual assumption in program evaluation is to assume that additional taxes are raised through income tax rate changes, and this has been assumed in this study.

Seminal studies that have evaluated the marginal welfare cost of raising additional tax revenue (known as the marginal cost of public funds (MCF)) mostly relate to the United States (Browning 1976, Stuart 1984, Ballard 1985, Browning 1987). Estimates have ranged from zero marginal cost to well over 100%. This wide range has been due to alternative models used (partial versus general equilibrium), alternative parameter estimates, and assumptions on the adjustment of employment relative to changes in tax rates (labour supply elasticities).

The deadweight loss arising from taxation used in this study was derived from the Productivity Commission (2003), who estimated a rate of 27.5%. This means that for every one dollar of additional tax revenue raised there is an associated deadweight loss of $0.275. Multiplying this rate by total direct health care system costs associated with ACS ($1.8 billion), the deadweight loss is estimated at $486.2 million.

22

In general it is more efficient to place taxes on markets that are relatively inelastic.


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4 Burden of disease

People who suffer from unstable angina or have had an acute myocardial infarction (AMI) may experience a considerable loss in both the length and quality of life. As a result, the total stock of health capital in Australia will be reduced, by an amount that reflects the prevalence of these conditions as well as their severity. As people place a value on their health, for example by paying for treatments that increase their health, it is possible to assign a value to the loss in the stock of health capital due to specific conditions.

This chapter estimates the value of the reduction in health in the Australian population aged 35+ from disability and premature death associated with ACS. The reduction in health is estimated by using the burden of disease methodology, developed by the World Health Organisation, the World Bank and Harvard University. The value of this reduction is calculated by using the value of a statistical life year (VSLY), based on the best practice proposed by the Department of Finance and Deregulation (DoFD 2008).

4.1 Methodology used for measuring and valuing the burden of disease The burden of disease methodology was developed as a comprehensive measure of mortality and disability from diseases, injuries and risk factors for populations around the world in 1990, projected to 2020 (Murray and Lopez 1996). It uses a non-financial approach, where pain, suffering and premature mortality are measured in terms of Disability Adjusted Life Years (DALYs).

DALYs are a measurement unit that quantify the morbidity aspect as well as the premature death associated with various diseases and injuries (Murray and Acharya 1997). DALY weights are measured on a scale of zero to one, where a zero represents a year of perfect health and a one represents death. Other health states that result from specific diseases or injuries are given a weight between zero and one to reflect the quality of life that is lost due to a particular condition. A disability weight of, for example, 0.395 for people who survive a heart attack, is interpreted as a 39.5% loss in the quality of life relative to perfect health. The disability weights are pre-agreed on by a reference group convened at the WHO on the basis of a person trade-off method for measuring health state preferences (Murray and Acharya, 1997).

Under the DALY framework, the total burden of disease for an individual with a condition is the sum of the mortality and morbidity components associated with that condition, and includes the years of health life lost due to disability (YLDs) and the years of healthy life lost due to premature death (YLL). If the time preference for health is incorporated, a DALY can be represented as:

,

(1 )

a L

i t

i t a

t a

DwDALY

r

Where Dw is the DALY weight of the condition experienced by individual i, L is the residual life expectancy of the individual at age a, and t represents each year within that life expectancy. Aggregating the DALYs of all individuals with a particular condition produces the total burden of that disease on society:


55

,

0

tN

t i t

i

DALY DALY

where N is the prevalence of that condition at time t.

The DALY approach is not financial, and thus not directly comparable with monetary costs and benefits associated with a particular condition. In order to undertake an economic evaluation, a monetary conversion of the loss in healthy life is usually performed. This allows the determination of the total cost of a condition and also the comparison of this cost to the benefit from a particular health intervention. The monetary conversion involves applying a value of a statistical life year (VSLY) in perfect health to the total number of DALYs estimated for a particular condition. The VSLY emerges from estimates of a willingness to pay for a reduction in the risk of physical harm in the context of OHS policy, transport and airspace regulation and environmental policy. The VSLY essentially estimates how much society is willing to pay to reduce the risk of premature death, expressed in terms of a saving a statistical life year. In this report, a VSLY of $161,276 was used, as recommended by the Department of Finance and Deregulation (DoFD 2008).23

4.2 Burden of disease from ACS To quantify the loss resulting from premature mortality and loss of health associated with ACS, the Global Burden of Disease methodology was used. Disability weights for unstable angina and AMI are based on the weights derived in a study on the burden of cardiovascular disease in Australia (Vos and Begg, 2007). These are:

■ 0.238 for unstable angina; 24 and

■ 0.395 for AMI.

The total burden of ACS in Australia is calculated using the methodology outlined in section 4.1. The total burden of disease includes two components, the Years of healthy life Lost due to Disability (YLDs) and Years of Life Lost due to premature death (YLLs). Estimates of the projected number of ACS separations in Australia were derived from Chapter 1 of this report, and were used to calculate YLDs from ACS in 2008. YLLs from ACS were estimated using projections on the number of deaths associated with AMI for 2009 (also derived from Chapter 1)).25 The value of life lost was obtained by applying the VSLY to the residual life expectancy at the time of death26 and a discount rate of five per cent per annum (NHMRC 2001).

4.2.1 Years of healthy life lost due to disability

YLDs from ACS in Australia were calculated by multiplying the number of separations associated with unstable angina and AMI that did not result in death by the appropriate

23

As the recommended DoFD figure ($151,000) is expressed in 2007 prices, the VSLY was inflated to 2009 prices ($161,276) using inflation rates of 4.2% for 2007-08 and 2.5% for 2008-09 (ABS 2009a).

24 The disability weight for unstable angina was calculated as the middle point between the disability weights for

AMI and mild/moderate angina pectoris, reported by Vos and Begg (2007).

25 Data was available up to (and including) 2007, and was linearly extrapolated to 2008.

26 Residual life expectancies were obtained from the ABS Life Tables (ABS 2008b).


56

disability weight. It is assumed that the disability is experienced for three months following unstable angina and AMI (Vos and Begg, 2007). In order to obtain the total financial cost associated with the years of healthy life lost due to disability from ACS, total YLDs were multiplied by the VSLY. Table 4.1 summarises the estimated value of healthy life lost due to disability from ACS in Australia in 2009.

Table 4.1: Value of YLDs associated with ACS 2009

AMI Unstable angina Total

$(million) $(million) $(million)

35-44 17 8 25

45-54 76 35 111

55-64 88 51 138

65-74 133 71 203

75-84 220 86 306

85+ 185 62 246

Total 719 311 1,030

Source: Access Economics calculation based on ABS (2008a; 2008b)

AMI accounts for a much larger share of the disability burden, valued at around $719 million, while the burden associated with unstable angina is valued at around $311 million. This yields an estimate of around $1.0 billion for the total cost associated with the years of healthy life lost due to disability in 2009.

4.2.2 Years of healthy life lost due to premature death

The years of life lost due to premature death from ACS are shown in Table 4.2. The estimated total number of deaths associated with ACS in 2009 was 9,959. Using residual life expectancies at the time of death (ABS 2008b), the total YLLs from ACS are estimated at 98,733. Applying the VSLY and a discount rate of 5 per cent, the value of the years of life lost due to premature death associated with ACS is estimated at around $11.3 billion in 2009.

Table 4.2: YLLs from ACS 2009

Number of deaths YLLs Discounted value of life lost

$(million)

35–44 years 99 4,103 277

45–54 years 283 9,051 721

55–64 years 615 14,360 1,347

65–74 years 1,320 20,564 2,263

75–84 years 3,475 31,508 4,001

85 years and over 4,167 19,147 2,698

Total 9,959 98,733 11,307

Source: Access Economics calculation based on ABS (2008a; 2008b)

4.2.3 Value of a loss in the stock of health capital due to ACS

The total cost associated with the burden of disease consists of the burden associated with years of healthy life lost due to disability (YLD) and years of healthy life lost due to premature death (YLL). Using the estimates presented in the last two sections, the total cost is estimated to be $12.3 billion in 2009.


57

4.3 Burden of disease comparisons In order to compare the burden of disease associated with ACS to other conditions within Australia, DALYs estimated by AIHW for each condition for 2003 (AIHW 2007) were linearly extrapolated to 2009.

The total burden of disease in Australia in 2009 is summarised in Table 4.3. Cancer is the leading causes of the burden of disease in Australia, accounting for 19% of the total burden. This is closely followed by cardiovascular disease (including ACS), whose share of the total burden in 16%. However a large part of this burden is derived from ACS, accounting for around 22%. As a separate condition, ACS imposes the ninth largest burden of disease in Australia.

Table 4.3: Burden of disease in Australia 2009

Males Females Total

DALYs DALYs DALYs

Malignant neoplasms (all cancers) 287,682 253,354 541,035

Cardiovascular disease 249,289 221,776 471,065

- Acute Coronary Syndrome 60,462 44,656 105,119

Mental disorders 181,072 198,493 379,565

Nervous system and sense organ disorders 177,185 195,946 373,131

Chronic respiratory disease 102,152 96,468 198,620

Injuries 134,962 57,441 192,402

Diabetes mellitus (Type 1 and 2) 98,228 84,089 182,318

Musculoskeletal diseases 51,984 70,724 122,708

Genitourinary diseases 33,135 41,155 74,290

Diseases of the digestive system 29,744 30,754 60,498

Infectious and parasitic diseases 31,392 18,736 50,128

Acute respiratory infections 20,477 21,410 41,887

Endocrine and metabolic disorders 17,562 17,551 35,113

Neonatal causes 17,917 14,748 32,666

Congenital anomalies 18,384 14,069 32,453

Oral conditions 12,952 14,786 27,738

Skin diseases 11,008 11,774 22,781

Other neoplasms 4,982 6,709 11,692

Ill-defined conditions 4,314 7,041 11,355

Nutritional deficiencies 1,677 5,183 6,860

Maternal conditions 0 2,285 2,285

Source: AIHW (2007) and Access Economics calculations


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5 Summary of costs

Table 5.1 presents a summary of projected separations, deaths and costs in 2009. In total, AMI is expected to cost around $15.5 billion. The majority of these costs are associated with the burden of disease, accounting for around 78%, which is representative of the large amount of premature mortality associated with AMI. Total direct health care system costs and indirect costs are expected to total around $3.5 billion in 2009. The total cost per AMI event (cost per heart attack) is expected to average $281,000.

Unstable angina is expected to cost around $2.4 billion in 2009. However the burden of disease only comprises $311 million, or around 13%. The majority of costs are associated with direct and indirect costs, totalling around $2.1 billion. The total cost per unstable angina event is expected to average $74,000.

Table 5.1: Summary of estimated separations, deaths and costs 2009

AMI (Heart attack)

UA (Chest pain)

ACS

Deaths before reaching a hospital 7,536 0 7,536

Hospitalisations without deatha 45,115 32,452 77,567

Hospitalisations with death occurring later 2,423 0 2,423

Total hospitalisations 47,538 32,452 79,990

Events 55,074 32,452 87,526


Direct health care system costs 1,191 577 1,767

Productivity loss (reduced participation) 1,254 1,073 2,327

Productivity loss (premature mortality) 287 0 287

Informal care 411 280 691

Deadweight loss 328 159 486

Burden of disease (YLD) 719 311 1,030

Burden of disease (YLL) 11,307 0 11,307

Total costs 15,497 2,400 17,895

$ $ $

Cost per separation (direct costs only)b 25,000 18,000 22,000

Cost per event (all costs)b

281,000

74,000

204,000

Note: (a) Within 28 days of being admitted to hospital (b) Cost per separation and cost per event has been rounded to the nearest $1,000. Source: Access Economics


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6 The future of ACS management

It is clear from preceding chapters that ACS is a large source of disease burden and of direct and indirect costs in Australia. Due to the potential severity and speed of ACS symptoms, an efficient and effective treatment path is vital in reducing mortality and preventing future cases of cardiovascular disease and stroke.

Although separation rates for ACS are decreasing, demographic ageing and increasing health care costs will place a burden on ACS treatment. This presents an opportunity to invest in areas that will bring further gains to reduce the burden of disease associated with ACS, and to increase the efficiency of current health care resources. This chapter identifies areas where further investment to identify potential cost effective strategies in the treatment and management of ACS is warranted.

6.1 A multidisciplinary approach to ACS care Due to the nature of ACS, rapid diagnosis and immediate cardiac intervention are important in influencing clinical outcomes. According to the American College of Cardiology (ACC) and American Heart Association (AHA) ACS management guidelines, there needs to be a multidisciplinary continuum of care that stems from the initial onset of symptoms to post hospital discharge and rehabilitation. Corbelli et al (2009) suggests that without an integrated pathway of care, appropriate therapy may be underused in ACS patients because of limited application of best clinical practice as well as poor communication between health service providers.

To ensure early intervention for ACS patients in Australia, ambulances need to be equipped with proper facilities in order to minimise the time between the onset of symptoms and treatment. This includes defibrillators and remote ECG monitoring and thrombolytic capabilities. ECG readings should be transmitted to a cardiologist to confirm the patient has ACS. Paramedics should be trained to interpret ECGs and perform thrombolysis if appropriate so that treatment can be offered to patients immediately on-site or in the ambulance on the way to a hospital. A pre-hospital thrombolysis program is already being trialled in the Hunter region by Ambulance Services of NSW in conjunction with Hunter New England Health (and the John Hunter Hospital). A similar trial was conducted by the Queensland Ambulance Service in 2008

It is necessary to extend the treatment path beyond in-hospital care because patients with a history of ACS are more susceptible to recurrences of vascular or ischemic events. Rockson et al (2007) found that ACS increases the risk of future coronary, cerebrovascular, and peripheral arterial events, thereby increasing the risk of further cardiovascular morbidity and mortality. Furthermore, substantial benefits to the patient’s wellbeing can be generated through supporting patients’ spouses, carers and family members in all stages of the treatment pathway.

In a study of three Australian hospitals, Scott et al (2004) found that the quality of ACS care can be significantly improved. Suggestions included:

■ the introduction of simple in-hospital interventions such as patient education program for self-management;


60

■ reminder tools to support adherence to clinical guidelines;

■ performance feedbacks to generate discussion; and

■ ongoing improvements in care and facilitation of multidisciplinary approach to work practices.

Following quality improvement interventions, Scott et al (2004) found there were statistically significant improvements in timeliness of ECGs, prescription of coagulants at discharge and use of patient counselling and rehabilitation services.

6.2 A national ACS registry Despite the recognised advantages of evidence based care (Corbelli et al 2009) gaps between evidence based treatment strategies and actual practice currently exist within Australia. For example, Chew et al (2007) and Walters et al (2008) found that some ACS patients are undertreated and there are significant delays in patients receiving guidelines based treatment. Both studies highlight the persistent suboptimal utilisation of established pharmacological therapies. For example, Chew et al (2007) found that only 56.7% of patients with STEMI received all five secondary prevention medications recommended by the Australian Guidelines at hospital discharge. One reason suggested was the lack of recognition of patient risk factors due to limited data documenting patient management.

Although many clinical studies attempt to compare risk factors amongst patients, treatment methods and resulting outcomes, these are typically done within a controlled and sterilised environment. Consequently, results may differ when applied to the health care system (Tonkin 2001). In addition, most studies of ACS treatment pathways and health outcomes focus on international health care systems. It is difficult to extrapolate results from these studies to an Australian context because Australia has its own unique health care system, patient characteristics and behaviour, and health environment.

At present, Australia does not have a state wide database on ACS patients or care, let alone a uniform national ACS registry. Collection of comprehensive and consistent data at a local level across Australia is necessary in order for ACS treatment and outcomes to be measured. This will provide the opportunity to identify best practice and shift limited health care resources to those areas that are cost effective. Subsequent advantages are expected to include improved quality of care, more appropriate treatment, better health outcomes, and a reduction in costs relative to health gains.

To isolate factors that increase the risk of ACS and cause variations in treatment outcomes amongst different patients, data needs to be collected on those factors that impact individual health and treatment outcomes, both within and outside the health care system. Table 6.1 presents some of these factors. Ideally, an ACS registry would include pre-treatment data, data associated with treatment, and post treatment data such as the effectiveness of rehabilitation programs.

An ACS registry that collects comprehensive data across Australia could also be used to develop performance indicators. Performance indicators that measure the contribution of treatment stages to health outcomes could be used to identify best practice treatment strategies and develop best practice guidelines. They could also be used to determine the cost-effectiveness of current therapies and assist in identifying redundant therapies.


61

Table 6.1: Factors that impact on health and health outcomes

Individual Geographical Institutional

Type and severity of condition Residence of patient Capabilities of ambulance treatment

Co-morbidities (number and types)

Geographical status of hospital (e.g. urban versus rural)

Procedure type and complexity

Post operative complications Health region Devices and prosthetics used

Morbidity and mortality within one year of treatment

State/territory Qualifications and experience of staff

Health behaviours Access to treatment (symptom to treatment times)

Hospital facilities (e.g. surgical and post operative care)

Biomedical factors Access to hospital Hospital type (e.g. teaching)

Psychosocial factors Access to catheterisation laboratory

Rehabilitation program

Socioeconomic Access to rehabilitation Pharmaceuticals prescribed

Demographic

Cultural

Genetic

Adherence to clinical advice and use of medicines

Access to informal care

Co-payments

Source: Access Economics

Performance indicators could also allow comparisons to be drawn between the performance of clinicians and institutions within, and across, health regions. This will provide an opportunity for shared learning between clinicians and institutions identify reasons why some institutions perform better than others, and generate greater accountability within the health care system. Performance indicators could also assist in recognising outliers in the health care system, for example, the best and worst hospitals in terms of outcomes.

According to Tonkin (2001) and Scott (2008), an ACS registry is expected to lead to several advantages. These include:


62

■ Identification of trends in adverse outcomes as a result of PCI use;

■ a common set of indicators and definitions used amongst ACS management stakeholders;

■ identification of population groups that are underserved, such as Indigenous Australians, ethnic groups, those with co-morbidities including renal disease and diabetes, and the elderly;

■ improved management of ACS patients;

■ clinicians and health institutions become publically accountable; and

■ better information for patients to make informed choices about their treatment options.

One area that a national ACS registry will need to address is the lack of data on ACS treatment and health outcomes associated with Indigenous Australians. The most comprehensive data on Indigenous health and outcomes collected by the ABS still only relies on four jurisdictions for accurate Indigenous status identification. As a result, this makes it extremely difficult to analyse general trends in the burden of disease and treatment in the Indigenous population and impedes on the assessment of secular trends.

The high economic cost associated with ACS means there could be the potential for large effectiveness and efficiency gains through the development and use of an ACS registry. This is expected to lead to cost savings and better health outcomes for all ACS patients.

6.3 Rehabilitation According to the Australian Guidelines, initiating a comprehensive cardiac rehabilitation program post hospital discharge is important in the management of ACS, particularly in the secondary prevention of recurrent coronary heart disease.

Evidence shows that formal cardiac rehabilitation programs following an ACS event can reduce morbidity and mortality associated with an event, and the risk of a recurrent ACS event (Rockson et al 2007). Outpatient rehabilitation is particularly important because hospital stays are becoming shorter, thereby limiting the opportunities for inpatient education about risk reduction and lifestyle changes (Ades et al 2001). Briffa et al (2005) estimated that post-discharge rehabilitation (including an exercise regime to improve function capacity, education on lifestyle changes and pharmalogical treatment) compared to conventional care had an incremental cost-effectiveness ratio of $42,535 per quality-adjusted life year (QALY) saved, assuming that rehabilitation increased survival rates.27 This is within the acceptable range of the cost effectiveness threshold set by the World Health Organisation (WHO) and the Pharmaceutical Benefits Advisory Committee (PBAC).

Despite evidence pointing towards the cost effectiveness of cardiac rehabilitation, studies show that outpatient care is still underutilised in Australia. For example, Scott (2003) showed in a study of public and private hospitals in Queensland that the adoption of rehabilitation programs was slow, with only 30% of patients referred to an outpatient cardiac rehabilitation program after discharge. In another study of hospitals in various States by Walters et al (2008),

27

If we assume rehabilitation only impacts on the quality of life and not the length of life, then the incremental cost-effectiveness ratio increases to $70,580


63

less than 11% of patients across all centres included in the study were referred to cardiac rehabilitation.

The rehabilitation model of care generally comprises of three phases: inpatient care, outpatient care and maintenance care. Figure 6.1 outlines one model of care for rehabilitation. Following the inpatient clinical delivery of hospital treatment, patients should be referred to a pathway of services post hospital discharge that extend for a period of two years to enable permanent positive lifestyle changes. Rehabilitation programs should be designed on a case by case basis depending on the patient’s overall risk factor profile and co-morbidities (such as diabetes).

Figure 6.1: A model of care for rehabilitation

Source: Access Economics

An important part of inpatient care is education of the patient on their diagnosis, the type of procedure that was performed (i.e. type of stenting) as well as the time they may need off work after discharged from the hospital. Patients should be provided with the information necessary to fully understand the anatomy and pathology of their condition. In particular, patients should be given appropriate information about their risk factors (e.g. smoking, high cholesterol, overweight etc) and options for lifestyle changes to reduce the risk of an ACS event in the future.

Outpatient care consists of various components. The most important of these are rehabilitative and disease prevention programs (recommended for six to 12 weeks) preventing the progression of coronary and other diseases following from ACS. There should also be ongoing monitoring, support, education on self management, exercise and social support for patients and their carers. A pathway of services provided for an extended period of time (up to two years) would encourage permanent positive lifestyle changes. All programs should collect key performance indicators and report on these.

Rehabilitation

Program

Inpatient care

Outpatient care

Education on

diagnosis,

procedure type,

stenting (if any)

Education about

lifestyle changes

(eg dietary) and

risk factors

Follow-ups on each

patient on physical

and emotional

health after ACS

Ensure long term

medical therapy

Information on

the pathology and

anatomy of the

condition


64

For rehabilitation to be effective, comprehensive patient follow-up interviews after discharge are essential. At these follow-up interviews, the patient should undergo both physical (i.e. blood pressure, cholesterol tests, ECGs, emotional and psychological (i.e. signs of depression, anxiety, stress, financial hardships) assessments. The psychological impact following an ACS event is an important, but often neglected, area in the management of ACS. Thus, if patients can better understand their conditions, it can empower them to cope with their anxieties caused by ACS.

Returning to work can require an adjustment in duties and the conditions under which the employee works. Workplaces can also provide an excellent environment to facilitate the ongoing rehabilitation and lifestyle changes to prevent the reoccurrence of ACS events. No standardised national program exists to support this extension of rehabilitation practices, and employees and employers would benefit from such an initiative.

Another important aspect of rehabilitation involves long term therapy with a number of medications. Table 6.2 outlines recommended medications for ACS treatment. Antiplatelet agents including aspirin and adjunctive clopidogrel should be given to patients undergoing PCI and for all outpatients who have had an ACS event. Education about risk-factor management such as lifestyle changes and ensuring continued use of vasoprotective medication can extend overall survival rates and reduce reoccurrence of ACS and the costs associated with subsequent treatments (Briffa et al 2005).

Table 6.2: Recommended medications for ACS treatment

Medication Use

Aspirin Blood thinning antiplatelet agent

Clopidogrel Blood thinning antiplatelet agent used if stenting is present

β-Blockers In the event of a heart attack

ACE inhibitors Regulate blood pressure

Statin Lower cholesterol

Source: NHF and CSANZ (2006)

Rehabilitation also needs to involve a specialist nurse led multidisciplinary team of psychologists, dieticians, physiotherapists, exercise psychologists, pharmacologists and doctors. Once a full clinical assessment of patient’s health post-discharge has been completed, the patient can set goals for rehabilitation that are within the guidelines and develop an individualised dietary and exercise plan to prevent the progression of ACS or other coronary heart diseases.

There is a need for adequate resourcing of cardiac rehabilitation centres and hospitals to ensure places for patients to attend and complete rehabilitation programs, as well as strategies to improve compliance and adherence their long-term medications for the total rehabilitation program to be effective. There are several reasons for patients not attending rehabilitation programs or dropping out of a program or therapy, some of these include:

■ time restrictions imposed through work commitments;

■ transportation and mobility concerns, especially for the elderly and disadvantaged groups;


65

■ communication barriers for cultural and linguistically diverse groups; and

■ co-payment costs associated with some rehabilitation programs.

Women have lower participation rates for angiograms than men, as well as lower involvement in and completion rates of rehabilitation programs. Specific strategies are needed to increase the uptake of both angiogram rates and to place them in suitable cardiac rehabilitation programs.

These issues all hinder the effectiveness of cardiac rehabilitation and need to be addressed for outpatient and maintenance care to reach its full potential. Redfern et al (2007) found that patients who did not attend standard cardiac rehabilitation after discharge were commonly in the high risk factor groups (such as high cholesterol, overweight or obese, a smoker or have diabetes) or had poor knowledge about risk factors associated with ACS. Balady (2007) have found that factors shown to increase the compliance and adherence to cardiac rehabilitation programs include; having a strong medical recommendation to attend, meeting a member of the rehabilitation team whilst an inpatient, having an appointment or discharge plan for accessing local cardiac rehabilitation services, offering a mode of service delivery that suits the person’s schedule (e.g. full day programs, weekend programs, early morning classes). All these would allow patients to interact with other who have similar problems and reinforce the rehabilitation process.

Senes and Penm (2007) also found that a large proportion of patients discontinue their long-term medications despite their effectiveness in preventing a second heart attack or chest pain Stafford (2003). The reasons for therapy discontinuation are not clear, but may be due to the cost of medications, the side effects associated with a medication, poor understanding of their condition, and poor communication and coordination between health workers and patients (Senes and Penm 2007). Therapy discontinuation by patients increases the likelihood of a repeat ACS event. Subsequently, this increases the burden of disease and the costs associated with ACS. Further work relating to compliance and adherence within the framework of the Quality Use of Medicines in Cardiovascular Health project will enhance understanding of the reasons, and strategies for improvement. The UK’s National Institute of Health and Clinical Excellence (NICE 2009) advocate both a “bottom-up” approach targeting consumers and health professionals along with “top-down” (government) engagement.

Participation in rehabilitation should ideally be on a patient opt-out basis rather than an opt-in basis. If patients have limited access to formal rehabilitation services, individualised home based rehabilitation programs should be developed.

6.4 Next generation antiplatelet agents Antiplatelet drugs can reduce the likelihood of blood clots by targeting and inhibiting the activation of chemicals that cause platelet aggregation. They are recommended by the Australian Guidelines for the primary and secondary prevent of ACS, and in recent years have drawn the most attention in pharmaceutical research for ACS, hence the focus in this study. There are other evidence-based pharmacological therapies proven to prevent the progression and recurrence of ACS (see Figure 6.2). Of these, there have only been mild improvements in statins in the form of higher potencies and increased dosing for ACS patients.


66

Generally, platelets circulate in the blood to form blood clots (through platelet aggregation) and stop ongoing bleeding rapidly (for example, when you have a wound). Platelet activation and aggregation is triggered by specific receptors for each physiological stimuli (including vWF, Collagen, ADP, Thrombin and Thromboxan A2; see Figure 6.2). When these blood clots form in coronary arteries, it blocks the flow of blood to the heart. In general, antiplatelet drugs assist blood clots by targeting and inhibiting the activation of chemicals that cause platelet aggregation. These are recommended by the Australian Guidelines for the primary and secondary prevent of ACS.

The effectiveness of antiplatelet drugs in preventing further ischemic events and death is widely accepted (Antithrombotic Trialists’ Collaboration 2002; Krotz et al 2008). However, these have also been associated with increased bleeding, and the rates of recurrent ischemic events still remain quite high (Becker et al 2009). As seen in Figure 6.2 there are still numerous triggers that activate platelet aggregation. As such, it is possible for further gains to be made via novel antiplatelet drugs that take advantage of these other receptors in inhibiting platelet aggregation without increasing the risk of excessive bleeding.

Aspirin is the most widely used antiplatelet therapy and the Australian Guidelines recommend that it be prescribed indefinitely (in the absence of any contraindications). The Australian Guidelines also recommend that Clopidogrel be prescribed for up to 12 months after an ACS event, the exact duration of which depends on the presence and type of stenting. Both drugs work by inhibiting the chemicals that cause platelet aggregation. The dosage, efficiency and indications of established antiplatelet agents need to be constantly re-evaluated to improve their effectiveness.


67

Figure 6.2: Signalling pathways that activate platelets

Source: Krötz et al

A number of new generation antiplatelet drugs are currently being evaluated for their efficacy and safety (see Table 6.3). These drugs target previously unrecognised mechanisms of action for inhibiting platelet activity. Prasugrel, Cangrelor and AZD6140 are ADP P2Y12 inhibitors, the latter being reversible. SCH 530348 will be a completely new class of antiplatelet agents called thrombin receptor antagonists (TRAs). It blocks the PAR-1 platelet receptor from binding to thrombin, which is the most potent platelet agonist and thereby hinders platelet aggregation.

Table 6.3: Status of new antiplatelet agents

Agent Clinical studies

Mechanism of antiplatelet activity Current status

Prasugrel JUMBO-TIMI 26; TRITON-TIMI 38

Irreversible ADP P2Y12 inhibitor (oral) Phase 3 results published

Cangrelor Storey; Jacobsson

Reversible ADP P2Y12 inhibitor (only available intravenously)

Phase 2/3

AZD6140 Husted; DISPERSE-2

Reversible ADP P2Y12 inhibitor (oral) Phase 2/3

SCH 530348 Moliterno PAR-1 thrombin receptor antagonist Phase 2/3

Source: Wadhawan (2009) and Smyth et al (2009)


68

Clinical studies have shown some improvements to current therapies. For example, Prasugrel has been found to have a greater and a quicker inhibitory effect on platelet aggregation (Wadhawan 2009). However, these drugs are still being tested for their safety as one side effect of inhibiting platelet aggregation is a high risk of excessive bleeding. Other possible complications include lack of efficacy in some patients, significant variability in patient response and potential resistance to the drug (Shalito et al 2009).

Furthermore, various studies are looking at the effectiveness of combining various agents. Hankey and Eikelboom (2003) found that the addition of clopidogrel and glycoprotein IIb/IIIa reduces the risk of serious vascular events among patients with NSTEACS and among patients undergoing PCI by up to 30%. SCH 530348 is also being tested for use with current therapies rather than in place of them (Wadhawan 2009).

If new drugs are found to be safe, lead to substantial improvements in effectiveness, and are cost effective, they should become an important part of future ACS treatment in Australia.

Access Economics

June 2009


69

Appendix A: Epidemiology estimates and projections

Table A.1: Male AMI age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 37.7 40.8 46.7 43.5 70.9 46.4 42.0

40-44 102.0 116.3 92.8 79.3 98.0 79.8 67.9

45-49 186.8 168.0 184.6 165.3 201.5 202.3 197.9

50-54 313.4 293.0 290.3 273.3 302.5 299.6 312.8

55-59 465.2 385.4 404.4 422.2 485.1 379.3 343.9

60-64 655.9 739.5 569.6 506.0 539.7 575.0 493.8

65-69 885.4 869.1 1025.7 714.6 771.2 800.9 731.8

70-74 1401.7 1209.1 1108.4 1111.8 1330.4 1094.0 1077.4

75-79 1574.5 1737.0 1763.5 1629.3 1607.9 1754.9 1510.4

Note: Based on age groups between 35 and 79 years old in Perth Statistical Division, Western Australia Source: Emeritus Professor Michael Hobbs, pers. com. 07 May 2009

Table A.2: Female AMI age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 13.0 5.5 5.6 7.3 9.3 9.3 20.4

40-44 28.3 7.5 25.9 23.7 16.2 12.3 27.9

45-49 32.2 33.4 36.6 34.1 30.0 37.1 43.7

50-54 77.9 64.8 88.1 72.7 73.6 74.1 75.4

55-59 113.4 111.2 127.4 83.9 93.3 99.1 109.9

60-64 204.7 155.7 177.6 167.9 187.1 195.0 132.0

65-69 403.5 382.5 328.2 341.6 315.9 352.3 273.2

70-74 584.8 669.5 568.0 461.7 614.8 520.4 580.3

75-79 1000.1 849.9 835.3 1067.5 1039.8 1051.8 919.8



70

Table A.3: Male unstable angina age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 56.6 18.6 24.3 37.8 38.3 21.3 22.9

40-44 111.8 77.5 77.7 75.5 81.4 81.6 78.6

45-49 211.2 198.0 160.8 195.2 156.0 165.3 178.8

50-54 345.7 275.4 288.2 320.5 288.2 258.8 304.6

55-59 513.4 446.1 531.5 544.4 396.2 357.9 402.7

60-64 975.7 716.0 694.1 624.4 704.7 578.3 639.5

65-69 1177.4 1019.5 1100.3 1150.8 968.4 788.0 837.5

70-74 1531.2 1336.7 1481.4 1383.2 1361.7 1177.3 1082.6

75-79 1962.1 1729.5 1586.4 1727.2 1601.0 1409.2 1497.4


Table A.4: Female unstable angina age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 13.0 9.2 13.0 12.9 11.1 11.2 16.7

40-44 20.7 26.1 14.8 38.2 14.4 17.6 27.9

45-49 66.4 47.1 42.3 53.0 50.6 52.0 65.5

50-54 126.6 138.8 112.4 112.1 96.0 92.2 85.3

55-59 236.8 228.8 215.4 196.8 165.4 156.1 147.3

60-64 361.3 381.3 359.0 314.3 249.5 252.2 312.3

65-69 611.9 641.8 538.3 471.3 433.8 384.7 370.7

70-74 821.6 915.2 908.8 769.5 689.4 692.4 644.8

75-79 1441.3 1238.8 1264.1 1148.8 1147.0 988.7 971.5


Table A.5: Male ACS age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 94.29 59.39 70.96 81.28 109.19 67.63 64.87

40-44 213.83 193.86 170.49 154.72 179.33 161.42 146.44

45-49 397.95 365.98 345.38 360.5 357.51 367.59 376.7

50-54 659.14 568.43 578.46 593.81 590.75 558.43 617.38

55-59 978.6 831.56 935.9 966.53 881.23 737.16 746.52

60-64 1631.62 1455.51 1263.72 1130.41 1244.37 1153.36 1133.31

65-69 2062.73 1888.57 2126.07 1865.43 1739.6 1588.87 1569.3

70-74 2932.9 2545.76 2589.75 2494.91 2692.13 2271.31 2160.06

75-79 3536.54 3466.43 3349.81 3356.41 3208.88 3164.05 3007.81



71

Table A.6: Female ACS age standardised separations per 100,000

1998 1999 2000 2001 2002 2003 2004

35-39 26.03 14.72 18.53 20.19 20.37 20.56 37.16

40-44 48.99 33.6 40.62 61.9 30.51 29.9 55.73

45-49 98.64 80.51 78.88 87.05 80.6 89.08 109.22

50-54 204.43 203.58 200.49 184.81 169.6 166.31 160.67

55-59 350.18 339.95 342.74 280.74 258.69 255.16 257.21

60-64 565.95 536.99 536.62 482.21 436.62 447.22 444.23

65-69 1015.39 1024.31 866.52 812.83 749.69 736.96 643.85

70-74 1406.34 1584.7 1476.78 1231.23 1304.21 1212.77 1225.07

75-79 2441.32 2088.66 2099.46 2216.32 2186.73 2040.49 1891.28


Chart A.1: Male AMI separation rates and trends


y = 39.772e0.4061x

R2 = 0.9212

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79

Ag

e s

tan

da

rdis

ed

ra

tes

pe

r 1

00

,00

0


72

Chart A.2: Female AMI separation rates and trends


Chart A.3: Male unstable angina separation rates and trends


y = 8.9208e0.4968x

R2 = 0.9764

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79

Ag

e s

tan

da

rdis

ed

ra

tes

pe

r 1

00

,00

0

y = 17.414x2 - 39.872x + 68.297

R2 = 0.9874

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79

Ag

e s

tan

da

rdis

ed

ra

tes

pe

r 1

00

,00

0


73

Chart A.4: Female unstable angina separation rates and trends


y = 10.022e0.455x

R2 = 0.9584

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79

Ag

e s

tan

da

rdis

ed

ra

tes

pe

r 1

00

,00

0


74

Table A.7: Actual and projected ACS separation rates for males

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

35-39 94 59 71 81 109 68 65 73 72 71 70 69

40-44 214 194 170 155 179 161 146 137 128 119 110 101

45-49 398 366 345 361 358 368 377 360 359 357 355 354

50-54 659 568 578 594 591 558 617 576 571 567 562 557

55-59 979 832 936 967 881 737 747 734 700 667 633 600

60-64 1632 1456 1264 1130 1244 1153 1133 985 909 833 758 682

65-69 2063 1889 2126 1865 1740 1589 1569 1482 1394 1306 1218 1130

70-74 2933 2546 2590 2495 2692 2271 2160 2132 2033 1934 1835 1737

75-79 3537 3466 3350 3356 3209 3164 3008 2965 2882 2799 2716 2632

80-84 5867 5438 5113 5030 4814 4785 4535 4293 4099 3911 3719 3533

85-89 8306 7722 7255 7048 6600 6799 6390 6003 5729 5466 5201 4947

90-94 11865 11040 10380 9947 9069 9717 9064 8431 8040 7666 7296 6946

95-99 17141 15932 14999 14181 12526 14002 12975 11926 11358 10819 10296 9807

100+ 25064 23235 21904 20446 17425 20366 18763 17014 16175 15387 14635 13940 Source: Access Economics calculations


75

Table A.8: Actual and projected ACS separation rates for females

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

35-39 26 15 19 20 20 21 37 29 31 33 34 36

40-44 49 34 41 62 31 30 56 43 44 44 44 44

45-49 99 81 79 87 81 89 109 96 98 100 102 104

50-54 204 204 200 185 170 166 161 150 142 134 125 117

55-59 350 340 343 281 259 255 257 222 203 184 165 146

60-64 566 537 537 482 437 447 444 401 378 355 332 309

65-69 1015 1024 867 813 750 737 644 578 513 449 384 320

70-74 1406 1585 1477 1231 1304 1213 1225 1140 1088 1036 984 932

75-79 2441 2089 2099 2216 2187 2040 1891 1901 1841 1782 1723 1664

80-84 5091 5856 5157 4145 4446 4159 3088 3088 2790 2505 2231 1965

85-89 9026 10974 9401 7207 8025 7419 5063 5181 4622 4097 3603 3134

90-94 16012 20578 17146 12533 14484 13239 8305 8692 7658 6703 5821 5001

95-99 28417 38608 31282 21801 26140 23630 13626 14584 12688 10968 9407 7987

100+ 50458 72477 57097 37933 47178 42182 22362 24469 21025 17951 15211 12766



76

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Notes

Information about Acute Coronary Syndrome and its treatmentBaker IDI Heart and Diabetes Institute www.bakeridi.edu.au Tel: 1300 728 900

People with Acute Coronary Syndrome are at greater risk of a second heart attack or chest pain. It is critical that once a person has had a heart event they maintain their medication, rehabilitation and healthy lifestyle changes life-long to prevent another serious event. An ongoing commitment to research will also help to better understand the risk factors, the need for early intervention, and help to prevent the onset of serious disease.

Heart Support Australia Ltd www.heartnet.org.au Tel: 61 2 6280 7211

Adoption of healthy lifestyle behaviours including regular exercise, good nutrition and co-prescribed medications with participation in rehabilitation programs can all help prevent further heart attacks or chest pain. Join your local Heart Support Australia branch for self-management, support, information, encouragement and motivation and achieve your optimum health potential.

Heart Foundation www.heartfoundation.org.au Tel: 1300 36 27 87

Your heart needs care for life. Everyone can do something to help prevent themselves getting heart disease. Making small, steady changes to your lifestyle can help to prevent you getting heart disease.

Don’t ignore the warning signs of heart attack! Get help fast. Every minute counts. Call 000.

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