- Age-standardised rates
- Annual percentage change (APC)
- Australian standard population (2001)
- Conditional survival
- Confidence intervals
- Lifetime risk
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)
Mortality 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 mortality estimates were used .
Queensland estimated resident population data used in calculating rates were also obtained from the Australian Bureau of Statistics .
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 incidence and mortality 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 . 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 incidence or mortality 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 . 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 .
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 .
Conditional survival is the probability of surviving an additional y years given the person has already survived x years . 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 .
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%.
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).
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).
Mortality measures the number of deaths caused by a given condition within a specified population over a defined time period (usually one year). Similar to incidence, mortality can also be expressed as a rate (per 100,000 population), and these rates are often age-standardised to account for variation in the age structures of different populations (see Age-standardised rates).
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 cancers (incidence) and the length of time patients survive after being diagnosed. Even though two types of cancer might have similar incidence, 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 2016) 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 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 . Patients who were still alive at 31st December 2016 were considered censored.
Relative survival estimates can be calculated using either the period or cohort methods . Relative survival estimates shown were produced using the period approach, which is recognised as providing more up-to-date survival estimates .
The STATA strs command was used to generate the relative survival estimates . 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 mortality data obtained from the Australian Bureau of Statistics) was calculated based on the Ederer II method . 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.
Cancer ICD-O3 codes used
|All invasive cancers||C00 to C80 (excluding C44 (M805 to M811))|
|Anus & anal canal cancer||C21|
|Bone cancer||C40 to C41|
|Brain cancer||C70 to C72|
|Chronic myeloproliferative diseases||M995 to M996|
|Colorectal cancer||C18 to C20, C218|
|Connective tissue & peripheral nerves cancer||C47, C49|
|Endocrine glands cancer||C74 to C75|
|Floor of mouth cancer||C04|
|Gallbladder cancer||C23 to C24|
|Gynaecological cancers||C51 to C58|
|Head and neck cancers||C01 to C14, C30 to C32|
|Hodgkin lymphoma||M965 to M966|
|Kidney cancer||C64 to C66, C68|
|Leukaemia||M980 to M994 (excluding M9733/3)|
|Lung cancer||C33 to C34|
|Lymphoid leukaemia||M982 to M983|
|Lymphoma||M959 to M972|
|Melanoma||C44 and C80 (only M872 to M879)|
|Myeloid leukaemia||M984 to M993|
|Nasal cavity cancer||C30 to C31|
|Non-Hodgkin lymphoma||M959, M967 to M972|
|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|
|Penile cancer||C60, C63|
|Pyriform sinus & hypopharynx cancer||C12 to C13|
|Rectosigmoid junction & rectal cancer||C19 to C20|
|Retroperitoneum & peritoneum cancer||C48|
|Small intestine cancer||C17|
|Thymus, heart, mediastinum & pleura cancer||C37 to C38|
|Tongue cancer||C01 to C02|
|Tonsil & oropharynx cancer||C09 to C10|
|Unknown primary site cancers||C26, C39, C76 to C77, C80|
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.
- Australian Bureau of Statistics, 2018. Deaths, Australia 2017. 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 5 October 2018, from http://stat.data.abs.gov.au/Index.aspx?Queryid=457.
- Australian Bureau of Statistics, 2018. Australian Demographic Statistics, Mar 2018. Table 53 Estimated Resident Population by single year of age, Queensland. ABS Cat. No. 3101.0. ABS: Canberra. Retrieved 5 October 2018, from http://www.ausstats.abs.gov.au/ausstats.
- 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.
- National Cancer Institute, 2014. Joinpoint Regression Program, Version 4.1.1. Retrieved 16 September 2014, from http://surveillance.cancer.gov/joinpoint/.
- 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.
- 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.
- Dickman PW, Sloggett A, Hills M, et al., 2004. Regression models for relative survival. Statistics in Medicine, 23(1):51-64.
- 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.
- 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.
- Dickman PW, 2004. Estimating and modelling relative survival using Stata. Retrieved 25 Nov 2016, from http://www.pauldickman.com/rsmodel/stata_colon/.
- Ederer F, Axtell LM, Cutler SJ, 1961. The relative survival rate: a statistical methodology. National Cancer Institute Monographs, 6:101-121.