### Data Sources

### Methods

- Age-standardised rates
- Annual percentage change (APC)
- Australian standard population (2001)
- Conditional survival
- Confidence intervals
- Incidence
- Lifetime risk
- Prevalence
- Survival
- Age specific survival
- Crude probability of survival

### 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 a_{j} 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 2019) 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 2019 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 2015-2019 ‘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

- 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. - 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. - 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. - 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