Australian Heart Maps

Data Sources

The Australian Heart Maps draw on the following data sources:

• Hospital Separations (2012-18): AIHW National Hospital Morbidity Database (Customised data request)
• CHD mortality (2013-17): AIHW Mortality Over Regions and Time (MORT) books
• Obesity (2017-18) Public Health Information Development Unit (2020) Social Health Atlas (risk factor modelled estimates)
• Smoking (2017-18) Public Health Information Development Unit (2020) Social Health Atlas (risk factor modelled estimates)
• High Blood Pressure (2017-18) Public Health Information Development Unit (2020) Social Health Atlas (risk factor modelled estimates)
• Physical Inactivity (2017-18) Public Health Information Development Unit (2020) Social Health Atlas (risk factor modelled estimates)

Definitions

HOSPITALISATION OUTCOMES

Cardiovascular Disease (CVD)

Cardiovascular disease is an umbrella term encompassing heart disease, stroke and blood vessel disease. It includes the following ICD codes:

I00-I02, I05-I09, I10-I15, I20.0, I20.1, I20.8, I20.9, I21, I22, I23, I24, I25, I26-I28, I30-I32, I34-I38, I40-I41, I42-I43, I44-I45, I47, I48, I49, I50, I51, I60-I64, I65-I68, I70-I79, I80-I89, I95-I99, G45, Q20-Q28

Heart disease

Heart disease is a catch all phrase for a variety of conditions that affect the heart’s structure and function. It includes the following ICD codes:

I05-I09, I20.0, I20.1, I20.8, I20.9, I21, I22, I23, I24, I25, I26-I28, I30-I32, I34-I38, I40-I41, I42-I43, I44-I45, I47, I48, I49, I50, I51, Q20-Q28

Coronary heart disease

Coronary heart disease (CHD), previously called ischemic heart disease, is when coronary arteries get narrower and reduce the blood flow to the heart. It is the usual underlying cause of a heart attack. Cardiovascular disease (CVD) is the broader term that refers to all diseases and conditions involving the heart and blood vessels: coronary heart disease, stroke, cardiomyopathy/heart failure. It includes the following ICD codes:

I20.0, I20.1, I20.8, I20.9, I21, I22, I23, I24, I25

Heart attack

A heart attack (also known as a myocardial infarction) is a sudden blockage of an artery that supplies blood to an area of your heart. A heart attack is usually caused by coronary heart disease. It includes the following ICD code:

I21

Acute coronary syndrome (ACS)

ACS is a classified in heart maps as an admission with a principal diagnosis of either a heart attack of unstable angina. It includes the following ICD codes:

I20.1, I21

Heart failure

Heart failure happens when your heart muscle gets damaged, then becomes weak and doesn’t pump properly. Once the heart is damaged, it can’t heal. The damage can be caused by a heart attack, or long-term health problems like high blood pressure, diabetes or heart disease.  It can also be caused by cardiomyopathy, a disease of the heart muscle. It includes the following ICD code:

I50

A heart attack (also known as a myocardial infarction) is a sudden blockage of an artery that supplies blood to an area of your heart. A heart attack is usually caused by coronary heart disease. It includes the following ICD code:

I21

Valve disease

The heart has four valves which control blood flow between its four chambers. Valve disease occurs when the heart valve(s) are not functioning normally. This can include ‘leaky’ valves. It includes the following ICD codes:

I05-I09, I34-I38

Atrial fibrillation (AF)

AF is an irregular heart beat in which the timing of the contractions of the sections of the heart (atria and ventricles) are misaligned. This can be experienced as heart palpitations and is a risk factor in a number of CVD outcomes including stroke. It includes the following ICD code:

I48

Rhythm disorders

Rhythm disorders are a group of conditions that can cause the heart to beat in an abnormal rhythm (irregular heart beat). The most common irregular heart beat is atrial fibrillation, which is a major cause of stroke. They include the following ICD codes:

I44-I45, I47, I48, I49

All stroke

A stroke happens when blood cannot supply oxygen and important nutrients to brain cells. When brain cells do not get enough oxygen or nutrients, they die. This definition includes; ischaemic strokes (a blockage in a blood vessel), haemorrhagic stroke (caused by leaking or breakage of blood vessels) and transient ischaemic attacks (small ‘mini’ strokes). They include the following ICD codes:

I60-I64, G45

ASR

The Age-Standardised Rate (100,000 for mortality, per 10,000 for hospital admissions, and per 100 for risk factors). Unless otherwise stated, ASR is used when describing the data. ASR for admissions is age adjusted at the national level using the direct method with the Australian Estimated Resident Population on 30 June 2001 (ERP2001) as the standard.

No

The estimated number of people (aged 18 years and over) for the relevant heart health indicator.

Region

A ‘regions’ is defined here based on the Australian Statistical Geography Standard (ASGS) according to Statistical Area Level 3 (for the ACT) and Statistical Area Level 4 (for all other states and Territories). In the Australian Heart Maps there are 86 Statistical Areas Level 4 (SA4) and 10 Statistical Areas Level 3 (SA3) regions in the ACT (a total of 96 ‘regions’). The lowest national ranking for Regions is 96 (hospitalisations), 47 (Indigenous hospitalisations), 94 (CHD Mortality), 92 (Risk factors). The lowest national ranking across all LGAs nationally is 521 (heart related admissions); 423 (risk factors); and 495 (CHD mortality).

Hypertension

Data for hypertension is modelled from those in the 2017-18 National Health Survey whose blood pressure measured systolic Blood Pressure (BP) of 140 mmHg or more, or diastolic BP of 90 mmHg or more, irrespective of the use of BP medication. It is an estimate of un-managed hypertension, not prevalence per se.

Insufficient exercise

Data for Insufficient Exercise is modelled from the estimated number of people aged 18 years and over who undertook no or low exercise in the week previous to the National Health Survey (2017-18). Exercise level was calculated as ‘Duration of exercise (minutes) x Intensity factor (walking for fitness = 3.5, moderate = 5, vigorous = 7.5): low exercise refers to scores of less than 800.

Smoking

Smoking rates are estimated from the 2017-18 National Health Survey respondents who reported that they smoked manufactured (packet) cigarettes, roll-your-own cigarettes, cigars, and/or pipes at least once per week. It excludes chewing tobacco and smoking of non-tobacco products.

Obesity

Obesity is defined as BMI>30 and the rates here are modelled from the adults in the 2017-18 NHS sample who had their height and weight measured in the National Health Survey.

Blank cells or #

Where cells are blank or #, no data is available.

Acknowledgments

The Heart Foundation would like to acknowledge the following organisations:

• The Australian Institute of Health and Welfare (AIHW): in seeking clearance from the State/Territory Data custodians and for undertaking the preliminary data analysis.
• The Australian Institute of Health and Welfare (AIHW); for making the mortality data so accessible and for their collaborative approach to using the MORT data.
• State/Territory Health departments: for ongoing feedback relating to the project.
• Professor John Glover and his team at the Public Health Information Development Unit (Torrens University, SA) for their collaborative support and for making their risk factor data freely accessible.
• Statistical Consultant and Epidemiologist, Steve Vander Hoorn: for undertaking comprehensive statistical analysis and for developing the online mapping tool.
• The individuals and organisations who kindly participated in user-testing and provided valuable feedback on design and functionality.

The Heart Foundation would like to acknowledge the following organisations:

• The Australian Institute of Health and Welfare (AIHW): in seeking clearance from the State/Territory Data custodians and for undertaking the preliminary data analysis.

• The Australian Institute of Health and Welfare (AIHW); for making the mortality data so accessible and for their collaborative approach to using the MORT data.

• State/Territory Health departments: for ongoing feedback relating to the project.

• Professor John Glover and his team at the Public Health Information Development Unit (Torrens University, SA) for their collaborative support and for making their risk factor data freely accessible.

• Statistical Consultant and Epidemiologist, Steve Vander Hoorn: for undertaking comprehensive statistical analysis and for developing the online mapping tool.

• The individuals and organisations who kindly participated in user-testing and provided valuable feedback on design and functionality.