1.3. Discuss the use of epidemiology to explain the health status of Australians

1. Epidemiology

Epidemiology is the study of health and disease in populations. It looks for patterns and causes of illness, injury, and death, and it measures how often these events happen. Reports often compare groups by age, sex, location, or socioeconomic status.

Epidemiologists use data from hospitals, health services, surveillance systems, population surveys, and the register of births and deaths. This helps describe Australia’s health status using clear measures.

A strength of epidemiology is that it turns health into evidence you can compare across time and groups. This helps with priorities, prevention, and how health resources are shared. A limitation is that it often measures ill health more easily than positive health, so some parts of health (like daily functioning and wellbeing) can be missed or underrepresented.

Example: If a report shows the incidence of melanoma is rising in young adults in NSW, this suggests risk is increasing and can support stronger prevention (for example, targeted UV education and more shade in school sport settings). If mortality from melanoma is falling at the same time, this can suggest earlier detection and better treatment are working.

1.1. Mortality

Mortality means the number or rate of deaths in a population over a set time. It is often shown as a rate (for example, deaths per 100,000 people) so comparisons are fairer.

Mortality data helps explain health status by showing whether Australians are dying less often, dying later in life, or dying from different causes over time.

In Australia, many of the leading causes of death are non-communicable diseases. These often include coronary heart disease and dementia (including Alzheimer’s disease). Mortality data can also show major events. For example, during the pandemic period, COVID-19 changed patterns of death in Australia.

A key concept in mortality data is the age-standardised mortality rate. This uses age-standardisation to adjust for different age structures between populations. This matters because an older population will usually have more deaths, even if overall health is similar.

Another mortality indicator is life expectancy, which summarises death rates into an estimate of how long people are expected to live if current rates continue. Australia’s life expectancy at birth is high, which generally reflects low overall mortality.

Example: If two states have the same number of deaths, but one has an older population, the crude death count can be misleading. The age-standardised mortality rate helps you compare health status more fairly by reducing the impact of age differences.

1.2 Infant mortality

Infant mortality is the number of deaths of babies aged under one year per 1,000 live births. It is often used because it can reflect maternal health, the quality and access to antenatal and postnatal care, and broader living conditions.

Australia’s infant mortality rate is low by global standards. Long-term declines link to improvements in sanitation, nutrition, immunisation, pregnancy care, and newborn medical care. However, averages can hide differences between groups and places, so infant mortality can also highlight inequities within Australia.

Example: A low national infant mortality rate can suggest strong maternity and newborn care overall. If a report then shows higher infant mortality in very remote regions, it can point to an inequity in access to services and specialist care.

1.3 Morbidity

Morbidity means illness, injury, and disability in a population. Unlike mortality, it includes health problems that may not cause death but can reduce daily functioning and quality of life.

Morbidity is often explained using incidence and prevalence. It is also described using data from:

• health surveys (which may rely on self-report)
• hospitalisation and emergency care data (which can miss conditions managed outside hospital)
• health service use data
• disability and functional limitation measures

Australian morbidity data shows that many people live with long-term conditions. This helps explain health status by showing that even with high life expectancy, many Australians need ongoing management and support for health conditions.

Example: If mortality from heart disease falls but morbidity from cardiovascular disease rises, it can mean more people are surviving and living longer with the condition. This can increase demand for rehab and long-term care.

1.4 Incidence

Incidence is the number of new cases of a condition in a population over a set time (often each year). It helps show emerging problems, changing risk, and the impact of prevention.

Incidence can rise because risk is rising, because detection is better, or both. This is why incidence is important when you are explaining changes in health status over time.

Example: If a screening program expands, incidence can rise at first because more cases are found earlier. If the program works well, later reports may show reduced mortality.

1.5 Prevalence

Prevalence is the total number or proportion of people who have a condition at a given time. It helps estimate how widespread a condition is and the likely ongoing demand for healthcare and support services.

Prevalence is influenced by how many new cases occur (incidence) and how long people live with the condition (duration and survival).

Example: A condition can have low incidence but high prevalence if people live with it for many years. This matters for planning long-term services.

1.6 Incidence and prevalence together

Incidence and prevalence work best when used together:

• Incidence shows how quickly new cases are occurring.
• Prevalence shows how many people are living with the condition overall.

Prevalence can rise even if incidence stays steady, if people are living longer with the condition due to better treatment. Prevalence can stay low even with high incidence if the condition resolves quickly.

2. Using tables and graphs to explain health status

Tables and graphs in health reports are designed to show patterns that support explanation and comparison. To interpret them, you need to be clear about what is being measured and how the comparison is made.

Health reports usually tell you:

• the measure (mortality, morbidity, incidence, prevalence, life expectancy)
• the unit (counts, percentages, rates per 1,000 or per 100,000)
• the population (for example, all Australians or groups by age, sex, or location)
• whether the results are un-adjusted or age-standardised

Trends over time can reflect real changes in health, but they can also reflect changes in diagnosis, reporting, access to care, or population structure. Sudden spikes and drops need careful interpretation, especially during major events like a pandemic year.

Data notes also matter. For example, under-diagnosis can lower reported incidence and prevalence, and provisional cause-of-death data can change later after investigations are finalised.

2.1 Mortality – leading causes of death (2023)

This bar chart compares the top causes of death for males and females in 2023. It highlights that coronary heart disease remains the leading cause of death for males, whereas dementia is the leading cause for females. Lung cancer, cerebrovascular disease and COPD also feature prominently. Such data emphasises the ongoing dominance of chronic diseases in Australian mortality patterns and reveals sex‑specific differences that are important for targeted health strategies.

2.2 Infant mortality – Infant death rates by sex (1998‑2017)

This line graph tracks infant mortality rates by sex over two decades. It shows a steady decline in deaths among both boys and girls, with a reduction from roughly 5 deaths per 1 000 live births in 1998 to about 3.3 per 1 000 in 2017. Although both sexes show improvement, boys consistently have a slightly higher death rate than girls. These trends reflect broad improvements in maternal and infant health while also highlighting persistent gender differences.

2.3 Morbidity – Hospitalisation rates for chronic conditions (2012–13 to 2021–22)

This chart displays hospitalisation rates for chronic conditions across nine financial years. It illustrates that hospitalisations increased through the decade, reaching their highest point in 2018–19, before declining slightly in the following years. The lowest rate occurs in 2012–13. The visual underscores how chronic conditions place a substantial and growing demand on hospital services, and it helps identify when healthcare utilisation pressures were most acute.

2.4 Incidence – New cases of type 2 diabetes by age group (2000–2021)

This line graph plots new (incident) cases of type 2 diabetes per 1 000 people for different age groups over two decades. It reveals that incidence is markedly higher among those aged 65 and over, but across all age groups incidence has generally fallen since about 2008. The visual thus highlights both the age-related risk of type 2 diabetes and the encouraging downward trend in new diagnoses.

2.5 Prevalence – Proportion of people with type 2 diabetes by age group (2000–2022)

This chart shows the proportion of people living with type 2 diabetes across several age groups from 2000 to 2022. It demonstrates that prevalence rises steeply with age, exceeding 20 % among those aged 75 and over, while remaining below 2 % in adults under 45. Overall prevalence has increased gradually over the period, and the figure notes that approximately 5.9 % of Australian adults have type 2 diabetes, with notable variation by region

Prevalence – Proportion of people with type 2 diabetes by location (2018)

Figure CC.10 visualises how the prevalence of type 2 diabetes varies across Australia. The map plots age‑standardised rate ratios by Population Health Area, so each area’s rate is compared with the national average after adjusting for differences in age structure. The highest prevalence ratios in 2018 were observed in Barkly/Tenant Creek and Elsey/Gulf/Victoria River in the Northern Territory, and Molonglo in the ACT. This indicates that these locations have substantially higher prevalence than the national average, whereas many other areas fall around or below the average.

3. Strengths and limitations of epidemiology

Epidemiology is useful because it:

• provides clear measures of health status and change over time
• identifies leading causes of mortality and morbidity, which helps set priorities
• supports comparisons between groups and can highlight inequities
• helps evaluate interventions by tracking changes in incidence, prevalence, and mortality

Epidemiology has limits because it:

• can miss parts of health that are harder to measure (like lived experience and wellbeing)
• may not fully explain why patterns occur without also looking at determinants and context
• can hide differences within groups when only national averages are reported
• depends on data quality, definitions, and access to diagnosis and services, which can affect rates

Overall, epidemiology is essential for explaining the health status of Australians, but it works best when tables and graphs are read carefully and interpreted alongside context and determinants.