The Australian Medicare system has records of health services for all Australians, which are federally funded on a fee-for-service basis. Electronic Medicare records were accessed to identify all Australians aged 0-19 years on 1 January 1985, or born during the period 1 January 1985 to 31 December 2005. The cohort was followed to 31 December 2007 by electronic linkage to the Australian Cancer Database and the National Death Index maintained by the Australian Institute of Health and Welfare. Cancer diagnoses were based on ICD-10 (international classification of diseases, 10th revision) codes C00-C96, plus D45, 46, 47.1, and 47.3 for myelodysplasia and related bone marrow disorders. Socioeconomic status was derived from the SEIFA (socioeconomic indexes for areas) index based on residential post codes and validated by the Australian government.24
The SEIFA index was used to categorise each person into one of seven approximately equal sized groups, numbered by increasing socioeconomic index. If the index for an individual changed over time, we used the mean value.
We used records of all CT scan exposures in the Medicare database for people aged 0-19 years during the period 1 January 1985 to 31 December 2005. CT scans in state based tertiary hospitals were usually missed, because most of such services are not funded on a fee-for-service basis, and are thus absent from Medicare records. Our study would also have missed CT exposures of cohort members that took place outside Australia, exposures before 1 January 1985 or after 31 December 2005, and exposures in the cohort after the age of 19 years. Records were de-identified before being made available for epidemiological analysis.
Each individual was entered into the study on the latest of the following dates: 1 January 1985, date of birth, or date first known to Medicare. Cohort members remained in the study until their exit date, which was the earliest of the following dates: 31 December 2007, date of death, or date of first cancer diagnosis. A CT scan was defined as an exposure if it occurred on or after the person’s entry date, on or before 31 December 2005, when the person was aged 0-19 years, on or before the person’s exit date, and at least one year before any diagnosis of cancer.
The exclusion period before a cancer diagnosis, referred to as the lag period, was introduced because of the possibility that the scan was part of the cancer diagnostic procedure. Most analyses were based on lag periods of one year, but we repeated the main analyses with lag periods of five and 10 years to explore the possibility of reverse causation. To calculate person years at risk, we assigned each person to the unexposed group from the date of entry until the transfer date (date of the first CT scan plus any lag period), and to the exposed group from the transfer date until the exit date (fig 1).
Fig 1 Schematic diagram showing how study members contributed to unexposed and exposed groups. All study members were classified as unexposed on entry to the study. Those who were exposed to a CT scan remained in the unexposed group for the duration of (more ...)
The primary analysis was of incidence rate ratios (IRRs) for exposed versus unexposed individuals by Poisson regression, using the number of person years as an offset, and with stratification by age (single years), sex, and year of birth (two year bands). We used likelihood ratio tests to assess the significance of departures of the IRR from unity. Tests for trend compared Poisson regression models with and without the covariate of interest (for example, the number of scans or the age at first exposure). Floating 95% confidence intervals for the IRR categorised according to the number of CT scans were calculated using the amount of information in each category.25
We estimated the excess number of cancers among the exposed cohort as (1−(1÷IRR)) multiplied by the observed number of cancers in exposed individuals, and we divided this quantity by the total number of person years among exposed individuals to estimate the absolute excess incidence rate (EIR) among the exposed group compared with the unexposed group. Trends in the EIR were tested by least squares regression with inverse variance weighting. Significance tests were two tailed. Procedures were programmed in Stata statistical software, release 12 (StataCorp).
The number of CT scans provided the simplest measure of a person’s radiation exposure. Because it was impossible to obtain individual machine parameters for all CT procedures, we estimated average effective doses26
per scan (in mSv) by site and year of scan, and by age. Effective doses were obtained from the published literature27
for specific ages (newborn; 1, 5, 10, 15 years; adult) and then mapped to the corresponding age band in the Medicare dataset. In Australian radiological practice, as in the UK,12
it was common to adjust machine parameters for the size or age (or both) of the patient to reduce the radiation dose, from 2001 onwards. We derived average effective doses in each age band for each CT category for the periods 1985-2000 and 2001-05. Owing to the lack of data available for paediatric effective doses for the earlier time period, we applied a scaling factor to adult effective doses to infer paediatric effective doses34
before 2001, taking into account the differences in scan length31
between adults and children and the variation in x ray absorption with different body sizes.30
Collective effective doses were estimated for the exposed population by cumulating the average doses by type of CT scan, year of scan, and age of the individual across all scans, after excluding those exposures that fell within the relevant lag period (one, five, or 10 years). We derived average organ doses for brain and red bone marrow (mGy) from local33
and international sources.13