Methods

Data Sources

Methods

Other

Data Sources

Queensland Cancer Register (QCR)

Details of all cancers diagnosed in Queensland are legally required to be included in the QCR under the Public Health Act 2005. Notifications of patients with cancer are received from all public and private hospitals and nursing homes. Queensland pathology laboratories are also required to provide copies of pathology reports for cancer specimens. Information regarding the deaths of persons with cancer is provided to the QCR from the Registrar of Births, Deaths and Marriages.

Basal and Squamous cell skin cancers were not included in the comparisons of cancer types. This is because the basal and
squamous cell skin cancers are not registered by the QCR (similar to the practice in most other cancer registries),
since many are treated in doctors’ surgeries using techniques that preclude histological confirmation.

Australian Bureau of Statistics (ABS)

Cancer death data for all causes of death for Queensland residents were obtained from the Australian Bureau of
Statistics. These data were used in relative survival calculations (see Survival). For all years, published and freely
available death estimates were used [1].

Queensland estimated resident population data used in calculating rates were also obtained from the Australian Bureau of
Statistics [2].

Methods

Age-standardised rates

Age-standardised rates attempt to adjust for variation in age structures in different populations (either different
geographical areas or the same population across time). There are two methods of age-standardisation – direct and
indirect.

All cancer diagnoses and cancer deaths trends were calculated using directly standardised rates. The method involves
applying age-specific rates from the population of interest (i.e. Queensland) to a standard population, which on this
website is the Australian Standard Population 2001 [3]. Five-year age groups up to 85 years and over were used for all
of the age-standardisation.

Annual percentage change (APC)

This is the annual increase or decrease in the cancer diagnoses and deaths trends over the specified period. Negative
APC values describe a decreasing trend and positive APC values describe an increasing trend. A trend is taken to be
statistically significant if the 95% confidence interval does not include zero.

APC values were calculated using a statistical method called joinpoint analysis, with software developed by the
Statistical Research and Applications Branch of the National Cancer Institute [5]. The joinpoint method evaluates
changing trends (both the direction and the magnitude of the trend) over successive segments of time. A joinpoint is the
point at which the linear segment changes significantly.

The analysis begins with the assumption of constant change over time (i.e. no joinpoint). Up to four joinpoints were
tested in each model, depending on the number of years of data available and the stability of the yearly estimates. The
selected trend line was the one with the fewest joinpoints which provided the best fit to the observed data, based on
Monte Carlo permutation tests [4].

Australian Standard Population (2001)

The standard population currently used for direct age-standardisation within Australia is the 2001 Australian resident
population, which is released by the Australian Bureau of Statistics [3].

Conditional survival

Conditional survival is the probability of surviving an additional y years given the person has already
survived x years [5]. It is calculated by dividing the relative survival at (x+y) years after diagnosis by the
relative survival at x years after diagnosis, while confidence intervals were calculated using a variation of
Greenwood’s formula [6].

Confidence intervals

All estimates are calculated with some degree of uncertainty. This uncertainty is typically reported in terms of a
confidence interval, which specifies a range of values in which the true data point is expected to occur with a given
level of certainty. For example, a 5-year survival rate may be estimated as 11.1% with a 95% confidence interval of
10.3%-12.0%. This means that there is a 95% probability that the true survival rate will be somewhere between 10.3% and
12.0%.

Incidence

The incidence of a particular disease is the number of new cases diagnosed in a specified population during a given time
period (usually one year). Incidence is also commonly expressed as a rate (e.g. per 100,000 population). Since the risk
of most cancers varies with age, it is common practice to age-standardise incidence rates to allow for more valid
comparisons between populations (see Age-standardised rates).

Lifetime risk

Cumulative risk is a measure used to estimate the risk of developing or dying of cancer up to the age of 75, 80 or 85,
respectively. It takes into account the removal of persons from the population of interest who have already been
diagnosed with or died from cancer. Commonly expressed as a ‘1 in n’ proportion, the cumulative risk is calculated as:

where aj are the age-specific rates (5-year age groups) per 100,000 for ages 0-74 (for age 75), 0-79 (for age
80), or 0-84 (for age 85).

QCSOL provides the cumulative risk up to the ages of 75, 80 and 85 years as an approximation of lifetime risk. An x in
100 variation is also supplied, calculated as the inverse of the cumulative risk multiplied by 100.

These calculations assume that the person experiences the current age-specific risk rates up to the age specified (e.g.
85), so do not account for any specific risk factors (such as smoking).

Prevalence

Prevalence represents the number of people who had a diagnosis of cancer in the past and are still alive at a specified
point in time. It is impacted by both the number of new cases and the length of time patients survive after being
diagnosed. Even though two types of cancer might have similar number of cases, if one cancer has low survival rates and
another cancer has higher survival rates, then the prevalence of the second cancer will be greater.

This website presents “limited duration” prevalence, which counts cases who remain alive at a given time point (e.g.
31st December 2017) as prevalent when they were diagnosed within a specific time period. Limited duration prevalence
estimates are presented for 1-, 5-, 10-, 20- and 30-year time periods. Note that persons diagnosed with cancer before
1982 (when the Queensland Cancer Register began operating) are not included in any prevalence estimates.

Survival

Survival time is defined as the length of time between when a person is diagnosed with a disease and when they die.
However, since the eventual survival time of everyone diagnosed with cancer is not known (for example they may still be
alive), statistical adjustments are required to take into account those unknown or censored survival times.

Relative survival was used to estimate the proportion of people who survived for different lengths of time. Relative
survival compares the survival of people who have a particular disease or condition against the expected survival of a
comparable group from the general population, taking into account age, sex and year of diagnosis. The method does not
require knowledge of the specific cause of death, only knowledge of whether the patient has died. Relative survival is
the most commonly presented measure of cancer survival when using data from population-based cancer registries [7].
Patients who were still alive at 31st December 2017 were considered censored.

Relative survival estimates can be calculated using either the period or cohort methods [8]. Relative survival estimates
shown were produced using the period approach, which is recognised as providing more up-to-date survival estimates [9].

The STATA strs command was used to generate the relative survival estimates [10]. This uses a life table (or actuarial)
method for calculating observed survival. This approach involves dividing the total period of observation into a series
of discrete time intervals. The survival probabilities were then calculated for each of these intervals, and these were
multiplied together to get the estimate for observed survival. Expected survival (based on total Queensland deaths data
obtained from the Australian Bureau of Statistics) was calculated based on the Ederer II method [11]. Three-year
averages for expected survival were used to minimise the effects of year to year variation. Relative survival was then
obtained from the ratio of observed survival to expected survival.

Age specific survival

5 year relative survival estimates for age groups 0-49, 50-64, 65-79, 80-89 are presented for 2013-2017 ‘at risk’ period
(workbook only).

Crude probability of survival

The crude, or actual, probability of survival is a statistical measure that describes the average probability of a
person diagnosed with a cancer either dying from the cancer within a certain timeframe after diagnosis, dying from other
causes, or being alive after that timeframe. These are the probabilities averaged over all patients.

These calculations don’t require information about the actual cause of death, but rather use the relative survival
framework, so compare the observed survival with the expected survival in the general population. Crude probabilities of
survival are reported in terms of numbers for a group of 100 patients.

As an example, for a 5-year crude probability, of 100 people diagnosed with a cancer, on average, 65 of them are likely
to be alive five years after diagnosis, 28 are expected to have died from the diagnosed cancer within 5 years of
diagnosis while 7 are expected to have died from a cause other than the diagnosed cancer during that time.

Other

Cancer ICD-O3 codes used

 All invasive cancers  C00 to C80 (excluding C44 (M805 to M811))
 Anus & anal canal cancer  C21
 Bladder cancer  C67
 Bone cancer  C40 to C41
 Brain cancer  C70 to C72
 Breast cancer  C50
 Cervical cancer  C53
 Chronic myeloproliferative diseases  M995 to M996
 Colon cancer  C18
 Colorectal cancer  C18 to C20, C218
 Connective tissue & peripheral nerves cancer  C47, C49
 Endocrine glands cancer  C74 to C75
 Eye cancer  C69
 Floor of mouth cancer  C04
 Gallbladder cancer  C23 to C24
 Gum cancer  C03
 Gynaecological cancers  C51 to C58
 Head and neck cancers  C01 to C14, C30 to C32
 Hodgkin lymphoma  M965 to M966
 Kaposi sarcoma  M914
 Kidney cancer  C64 to C66, C68
 Larynx cancer  C32
 Leukaemia  M980 to M994 (excluding M9733/3)
 Lip cancer  C00
 Liver cancer  C22
 Lung cancer  C33 to C34
 Lymphoid leukaemia  M982 to M983
 Lymphoma  M959 to M972
 Melanoma  C44 and C80 (only M872 to M879)
 Mesothelioma  M905
 Myelodysplastic diseases  M998
 Myeloid leukaemia  M984 to M993
 Myeloma  M973
 Nasal cavity cancer  C30 to C31
 Nasopharynx cancer  C11
 Non-Hodgkin lymphoma  M959, M967 to M972
 Oesophageal cancer  C15
 Other female genital organs cancer  C52, C55, C57 to C58
 Other lip, oral cavity & pharynx cancer  C14
 Other lymphatic cancers  M974 to M976
 Other major salivary glands cancer  C07 to C08
 Other parts of mouth cancer  C05 to C06
 Other skin cancer  C44 (excluding M805 to M811, M872 to M879)
 Other specified leukaemia  M994
 Ovarian cancer  C56
 Pancreatic cancer  C25
 Penile cancer  C60, C63
 Prostate cancer  C61
 Pyriform sinus & hypopharynx cancer  C12 to C13
 Rectosigmoid junction & rectal cancer  C19 to C20
 Retroperitoneum & peritoneum cancer  C48
 Small intestine cancer  C17
 Stomach cancer  C16
 Testicular cancer  C62
Thymus, heart, mediastinum & pleura cancer  C37 to C38
 Thyroid cancer  C73
 Tongue cancer  C01 to C02
 Tonsil & oropharynx cancer C09 to C10
 Unknown primary site cancers  C26, C39, C76 to C77, C80
 Unspecified leukaemia  M980
 Uterine cancer  C54
 Vulva cancer  C51

In 2007 an alternate method of classifying bladder cancers as invasive or in-situ was adopted. This new method is
consistent with other Australian Registries, and is based on the layer of the bladder involved. This has resulted in a
decreased number of invasive bladder cancers, and only affects data from 2007 onwards. However, the bladder cancer
ICD-O3 codes have not changed.

Reference

  1. Australian Bureau of Statistics, 2020. Deaths, Australia 2018. ABS.Stat Dataset: Deaths, Year of occurrence, Age
    at death, Age-specific death rates, Sex, States, Territories and Australia. ABS Cat. No. 3302.0. ABS: Canberra.
    Retrieved 1 August 2020, from http://stat.data.abs.gov.au/Index.aspx?Queryid=457.
  2. Australian Bureau of Statistics, 2020. Australian Demographic Statistics, Dec 2019. Table 53 Estimated Resident
    Population by single year of age, Queensland. ABS Cat. No. 3101.0. ABS: Canberra. Retrieved 23 July 2020, from
    https://www.abs.gov.au/statistics/people/population/national-state-and-territory-population/latest-release#data-download.
  3. Australian Bureau of Statistics, 2003. Population by age and sex – 2001 census edition. ABS Cat. No. 3201.0.
    ABS: Canberra. Retrieved 23 July 2007, from http://www.ausstats.abs.gov.au/ausstats.
  4. National Cancer Institute, 2014. Joinpoint Regression Program, Version 4.1.1. Retrieved 16 September 2014, from
    http://surveillance.cancer.gov/joinpoint/
  5. Baade PD, Youlden DR, Chambers SK, 2011. When do I know I am cured? Using conditional estimates to provide
    better information about cancer survival prospects. Med J Aust, 194(2):73-77.
  6. Skuladottir H, Olsen JH, 2003. Conditional survival of patients with the four major histologic subgroups of lung
    cancer in Denmark. J Clin Oncol, 21(16):3035-3040.
  7. Dickman PW, Sloggett A, Hills M, et al., 2004. Regression models for relative survival. Statistics in Medicine,
    23(1):51-64.
  8. Brenner H, 2002. Long-term survival rates of cancer patients achieved by the end of the 20th century: a period
    analysis. Lancet, 360(9340):1131-1135.
  9. Brenner H, Gefeller O, Hakulinen T, 2004. Period analysis for ‘up-to-date’ cancer survival data: theory,
    empirical evaluation, computation realisation and applications. European Journal of Cancer, 40:326-335.
  10. Dickman PW, 2004. Estimating and modelling relative survival using Stata. Retrieved 25 Nov 2016, from http://www.pauldickman.com/rsmodel/stata_colon/.
  11. Ederer F, Axtell LM, Cutler SJ, 1961. The relative survival rate: a statistical methodology. National Cancer
    Institute Monographs, 6:101-121.
  12. Lambert PC, Dickman PW, Nelson CP, Royston P, 2010. Estimating the crude probability of death due to cancer and
    other causes using relative survival models