Age-standardised rates (ASR)

A crude rate provides information on the number of, for example, new cases of cancer or deaths from cancer by the population at risk in a specified period. No age adjustments are made when calculating a crude rate. Since the risk of cancer heavily depends on age, crude cancer incidence and mortality rates are not as suitable for looking at changes over time or making comparisons between different population groups if there are differences in those populations’ age structures.

More meaningful comparisons can be made using ASRs, with such rates adjusted for age in order to facilitate comparisons between populations that have different age structures—for example, between Indigenous Australians and other Australians. This standardisation process effectively removes the influence of age structure on the summary rate.

There are two methods commonly used to adjust for age: direct and indirect standardisation. In this report, the direct standardisation approach presented by Jensen and colleagues (1991) is used. To age-standardise using the direct method, the first step is to obtain population numbers and numbers of cases (or deaths) in age ranges—typically 5-year age ranges. The next step is to multiply the age-specific population numbers for the standard population (in this case, the Australian population as at 30 June 2001) by the age-specific incidence rates (or death rates) for the population of interest. The next step is to sum across the age groups and divide this sum by the total of the standard population to give an ASR for the population of interest. Finally, this is expressed per 100,000 population in this report.

Age-specific rates

Age-specific rates provide information on the incidence of a particular event in an age group relative to the total number of people at risk of that event in the same age group. It is calculated by dividing the number of events occurring in each specified age group by the corresponding ‘at-risk’ population in the same age group and then multiplying the result by a constant (for example, 100,000) to derive the rate. Age-specific rates are often expressed per 100,000 population.

Australian Cancer Database

All forms of cancer, except basal and squamous cell carcinomas of the skin, are notifiable diseases in each Australian state and territory. This means there is legislation in each jurisdiction that requires hospitals, pathology laboratories and various other institutions to report all cases of cancer to their central cancer registry. An agreed subset of the data collected by these cancer registries is supplied annually to the AIHW, where it is compiled into the ACD. The ACD currently contains data on all cases of cancer diagnosed from 1982 to 2016 for all states and territories with the exception of 2016 Northern Territory data.

Cancer reporting and registration is a dynamic process, and records in the state and territory cancer registries may be modified if new information is received. As a result, the number of cancer cases reported by the AIHW for any particular year may change slightly over time and may not always align with state and territory reporting for that same year.

For more information on the ACD please see the ACD 2016 Data Quality Statement.

Estimating 2016 cancer incidence for the Northern Territory

Cancer incidence for the Northern Territory in 2016 was estimated using the following process.

The total number of in-scope cases for NT for 1982–2015 is well-modelled by a quadratic function of the year of diagnosis (adjusted R2 = 0.9884). The total number of in-scope cases for 2016 was estimated by extrapolating this function to 2016. These cases were then allocated pro-rata to various strata on the basis of the number of cases observed in those strata in NT in the pooled years 2011–2015. The strata were the cross product of sex by Indigenous status by single-year age at diagnosis by topography by morphology by SA2 at diagnosis (2011 ASGS). The estimates within each stratum were then aggregated to obtain estimates for larger categories.

International Classification of Diseases for Oncology (ICDO)

Cancers were originally classified solely under the ICD classification system, based on topographic site and behaviour. However, during the creation of the Ninth Revision of the ICD in the late 1960s, working parties suggested creating a separate classification for cancers that included improved morphological information. The first edition of the ICD-O was subsequently released in 1976 and, in this classification, cancers were coded by both morphology (histology type and behaviour) and topography (site).

Since the First Edition of the ICD-O, a number of revisions have been made, mainly in the area of lymphoma and leukaemia. The current edition, the Third Edition (ICD-O-3), was released in 2000 and is used by most state and territory cancer registries in Australia, as well as by the AIHW in regard to the ACD.

National Mortality Database

The AIHW National Mortality Database (NMD) contains information provided by the Registries of Births, Deaths and Marriages and the National Coronial Information System—and coded by the ABS—for deaths from 1964 to 2018. Registration of deaths is the responsibility of each state and territory Registry of Births, Deaths and Marriages. These data are then collated and coded by the ABS and are maintained at the AIHW in the NMD.

In the NMD, both the year in which the death occurred and the year in which it was registered are provided. For the purposes of this report, actual mortality data are shown based on the year the death occurred, except for the most recent year (namely 2018) where the number of people whose death was registered is used. Previous investigation has shown that the year of death and its registration coincide for the most part. However, in some instances, deaths at the end of each calendar year may not be registered until the following year. Thus, year of death information for the latest available year is generally an underestimate of the actual number of deaths that occurred in that year.

In this report, deaths registered in 2015 and earlier are based on the final version of cause of death data; deaths registered in 2016, 2017 and 2018 are based on revised and preliminary versions, respectively, and are subject to further revision by the ABS.

The data quality statements underpinning the AIHW NMD can be found on the following ABS internet pages:

For more information on the AIHW NMD see Deaths data at AIHW.

Population Data

Throughout this report, population data were used to derive rates of, for example, cancer incidence and mortality. The population data were sourced from the ABS using the most up-to-date estimates available at the time of analysis.

To derive its estimates of the resident populations, the ABS uses the 5-yearly Census of Population and Housing data and adjusts it as described here:

  • All respondents in the Census are placed in their state or territory, Statistical Local Area and postcode of usual residence; overseas visitors are excluded.
  • An adjustment is made for persons missed in the Census.
  • Australians temporarily overseas on Census night are added to the usual residence Census count.

Estimated resident populations are then updated each year from the Census data, using indicators of population change, such as births, deaths and net migration. More information is available from the ABS website.


Limited-duration prevalence is expressed as N-year prevalence throughout this report. N-year prevalence on a given index date—where N is any number 1, 2, 3 and so on—is defined as the number of people alive at the end of that day who had been diagnosed with cancer in the past N years. For example:

  • 1-year prevalence is the number of living people who were diagnosed in the past year to 31 December 2015
  • 5-year prevalence is the number of living people who were diagnosed in the past 5 years to 31 December 2015. This includes the people defined by 1-year prevalence.

Note that prevalence is measured by the number of people diagnosed with cancer, not the number of cancer cases. An individual who was diagnosed with two separate cancers will contribute separately to the prevalence of each cancer. However, this individual will contribute only once to prevalence of all cancers combined. For this reason, the sum of prevalence for individual cancers will not equal the prevalence of all cancers combined.

Projections - Estimating the incidence of cancer

Estimates of national incidence in 2017–2020 was estimated by projecting the sex- and age-specific incidence rates observed in Australia during 2007–2016. The time series were stratified by the following variables:

  • sex
  • 5-year age group (0–4, …, 85–89, 90+)
  • 4-character ICD-O-3 topography code (C00.0, …, C80.9)
  • 4-digit ICD-O-3.1 histology code (8000, …, 9992).

For each time series, the process was as described below:

  • least squares linear regression was used to find the straight line of best fit through the time series
  • if the slope was positive, the straight line of best fit was extrapolated to obtain the estimate of the 2017 rate
  • if the slope was negative, the time series floor was set to 0
  • the estimated incidence rates for 2017 were then multiplied by the Estimated Resident Populations for 2017 to obtain the estimated incidence numbers.

Note the following:

  • estimates were made for Australia as a whole, not for individual jurisdictions
  • instead of using the topography and histology codes to define the cancer groups, ICD-10 codes were used (for example breast or melanoma of the skin as well as groupings such as head and neck cancers which is a consolidation of cancers of the lip, tongue, mouth, salivary glands, oropharynx, nasopharynx, hypopharynx and other sites in the pharanyx).
  • the incidence estimates made for 2016 for Northern Territory were treated as real data for the purposes of estimating Australian incidence for 2017–2020
  • the 10 years of incidence data used as the baseline were 2007–2016
  • for populations, the ABS Estimated Resident Populations were used for 2007–2018, and the ABS population projection series B for 2019–2020 (ABS 2018).

Projections - Estimating the mortality of cancer

This method is the same as the incidence projections with the exceptions that:

  • the 10-year baseline for incidence is 2007-2016 while the baseline for mortality is 2009-2018.
  • Northern Territory 2016 data is obtained from the NMD and is not estimated

Relative survival

Relative survival is a measure of the survival of people with cancer compared with that of the general population. It is the standard approach used by cancer registries to produce population-level survival statistics and is commonly used as it does not require information on cause of death. Relative survival reflects the net survival (or excess mortality) associated with cancer by adjusting the survival experience of those with cancer for the underlying mortality that they would have experienced in the general population.

Relative survival is calculated by dividing observed survival by expected survival, where the numerator and denominator have been matched for age, sex and calendar year.

Observed survival refers to the proportion of people alive for a given amount of time after a diagnosis of cancer; it is calculated from population-based cancer data. Expected survival refers to the proportion of people in the general population alive for a given amount of time and is calculated from life tables of the entire Australian population. (Ideally these life tables should be restricted to the population of Australians who do not have cancer but such life tables are unavailable. It is standard practice around the world to use life tables for the entire population.)

A simplified example of how relative survival is interpreted is shown in Figure G1. Given that 6 in 10 people with cancer are alive 5 years after their diagnosis (observed survival of 0.6) and that 9 in 10 people from the general population are alive after the same 5 years (expected survival of 0.9), the relative survival of people with cancer would be calculated as 0.6 divided by 0.9, which is 0.67. This means that individuals with cancer are 67% as likely to be alive for at least 5 years after their diagnosis as are their counterparts in the general population.

Observed survival is 6 out of 10, i.e. 0.6. Expected survival is 90 out of 100, i.e. 0.9. Therefore relative survival is 0.6 divided by 0.9, which is 0.67, or 67%25.

The survival statistics in this report were produced using a modified version of a SAS program written by Dickman (2004) and employed the period method (Brenner and Gefeller 1996) with 1-year intervals. Observed survival was calculated from data in the ACD. Expected survival was calculated using the Ederer II method whereby matched people in the general population are considered to be at risk of death until the corresponding cancer patient dies or is censored (Ederer and Heise 1959).

Calculation of conditional relative survival

Conditional survival is the probability of surviving j more days, given that an individual has already survived i days. It was calculated using the formula:

S of j given i equals S of i plus j divided by S of i. Variance of S of j given i equals the sum from k equals i plus 1 to i plus j of dk divided by rk times rk minus dk. Cumulative rate equals 5 times the sum of the age-specific rates times 100 divided by 100,000. Cumulative risk equals 1 minus e to the minus cumulative rate divided by 100. n equals 1 divided by the cumulative risk.

The 95% confidence intervals were constructed assuming that conditional survival estimates follow a normal distribution.


In this report, estimates of cancer risk are not included. However, in a forthcoming update to this report, estimates of cancer risk calculated using both methods described below will be included so different estimates of risk are available to be used for different purposes.

The risk estimates that will be added to this report in a forthcoming update are average risks across the whole population. A specific individual’s risk could be lower or higher than the population average depending on their own particular genetic and environmental risk factors.

There are various methods that can be used to calculate the risk of developing (or dying from) cancer. Different methods are appropriate for different purposes.

In previous cancer reports the AIHW has calculated risk using the method recommended by the World Health Organization’s International Agency for Research on Cancer (IARC) and the International Association of Cancer Registries (IACR).  This method is best suited for international comparisons because it is able to be used for most countries and does not adjust for competing mortality (the chance of dying from non-cancer causes before developing, or dying from, cancer). As these risk estimates are not adjusted for competing mortality they are comparable for different countries even if they have very different non-cancer mortality patterns.

An alternative method calculates the risk of developing (or dying from) cancer adjusted for competing mortality. The advantage of this method is that it has a ‘real world’ interpretation in the country to which it applies. For example, if the risk of developing a certain cancer by age 85 is reported as 10%, this means it is estimated that 10% of all Australians will develop that cancer before their 85th birthday. The figure of 10% has been derived by taking into account both the incidence rate of the cancer as well as the death rate from all causes. Another advantage of this method is that it is possible to calculate the lifetime risk whereas the mathematics behind the IARC-recommended method (see previous paragraph) always leads to a lifetime risk of 100%, which is not useful.

The IARC-recommended method of calculating risk at the population level and the method that adjusts for competing mortality are appropriate for different purposes. If the purpose is to provide an estimate of the average person’s ‘real world’ risk of cancer diagnosis or death, then it would be more appropriate to take competing mortality into account.

However, if the purpose is to compare cancer risk in two or more populations (or one population over time) then one method might be more appropriate than the other, or each method might provide different insights, depending on the question being asked. Two populations with the same age-specific cancer incidence (or death) rates would have the same cancer risk using the IARC-recommended method but potentially different risks when adjusted for competing mortality (i.e. they would be different if their non-cancer mortality risks were different).

In this report, estimates of cancer risk are not included. However, in a forthcoming update to this report, estimates of cancer risk calculated using both methods will be included so different estimates of risk are available to be used for different purposes.