The authors have declared that no competing interests exist.
Interpretation of data: FAH GP DDP CDJH KE. Critical revision of the manuscript for important intellectual content: DDP GP CDJH KE. Study supervision: DP. Conceived and designed the experiments: FAH. Analyzed the data: FAH. Wrote the paper: FH.
Data are limited on cancer outcomes in adolescents and young adults.
Based on data from the Western Australian Data Linkage System, this study modelled survival and excess mortality in all adolescents and young adults aged 15–39 years in Western Australia who had a diagnosis of cancer in the period 1982–2004. Relative survival and excess all-cause mortality for all cancers combined and for principal tumour subgroups were estimated, using the Ederer II method and generalised linear Poisson modelling, respectively.
A cancer diagnosis in adolescents and young adults conferred substantial survival decrement. However, overall outcomes improved over calendar period (excess mortality hazard ratio [HR], latest versus earliest diagnostic period: 0.52, trend p<0.0001). Case fatality varied according to age group (HR, oldest versus youngest: 1.38, trend p<0.0001), sex (HR, female versus male: 0.66, 95% confidence interval [CI] 0.62–0.71), ethnicity (HR, Aboriginal versus others: 1.47, CI 1.23–1.76), geographical area (HR, rural/remote versus urban: 1.13, CI 1.04–1.23) and residential socioeconomic status (HR, lowest versus highest quartile: 1.14, trend p<0.05). Tumour subgroups differed substantially in frequency according to age group and sex, and were critical outcome determinants.
Marked progressive calendar-time improvement in overall outcomes was evident. Further research is required to disentangle the contributions of tumour biology and health service factors to outcome disparities between ethno-demographic, geographic and socioeconomic subgroups of adolescents and young adults with cancer.
Cancers in adolescents and young adults (AYAs), commonly defined as persons aged 15–39 years
Compared with children and older adults, patients in the AYAs age group have reportedly experienced little or no improvement in cancer survival in more than two decades,
Data on all individuals first diagnosed with invasive cancer or lymphohaematopietic malignancy at 15–39 years of age during the period 1982–2004 were extracted from the Western Australian Data Linkage System (WADLS). Notification of all cancer diagnoses has been a statutory requirement for all public and private hospitals and pathology services in WA since 1981
Routine and unprecedentedly accurate geocoding of health records is a unique feature of the WADLS. This enables anonymised identification of the residential location of patients (including categorisation of remoteness), from which socio-economic status (SES) can be inferred. SES was measured using the Index of Relative Socio-economic Disadvantage (IRSD), which is based on Australian census data elements, including the prevalence of low income, low educational attainment, unemployment, rented dwellings, one-parent families, and other measures of social disadvantage such as prevalence of poor English language fluency
Relative survival ratio (RSR) was used to estimate disease-specific survival. RSR is defined as the ratio of the observed survival in the diseased individuals under study to the expected survival of the underlying general population in WA according to sex, age and calendar year of death. WA population estimates were supplied by the ABS
Ethics approval for this study was obtained from the University of Western Australia Research Ethics Committee (reference number: RA/4/1/2228). The Ethics Committee waived the need for participants’ written informed consent as this was a minimal-risk retrospective study, exclusively based on data extraction from administrative databases, and it would not be feasible to get patients’ consent for access to all charts. According to Australia human research law, informed consent can be waived in cases for which recording informed consent is not possible, provided that a justification is registered and an Ethics Committee gives approval. The data were analysed anonymously.
There were 10,266 incident cases of malignant neoplasms reported in WA among AYAs aged 15–39 years in 1982–2004. Based on all-cause mortality, the proportions of AYA cancer patients alive at 1, 5 and 10 years post-diagnosis were 91.8%, 75.6% and 49.8%, respectively. The median follow-up time was 8.2 years (interquartile range: 12 years).
No. of cases, (%) | Sex ratio(M:F) | MicroscopicVerification (%) | DCO(%) | ||
|
10266 | (100) | 0.7 | 98.6 | 0.06 |
|
384 | (3.7) | 1.5 | 96.4 | 0 |
|
806 | (7.9) | 1.4 | 97.9 | 0.1 |
|
350 | (3.4) | 1.3 | 87.7 | 0 |
|
148 | (1.4) | 1.4 | 98.8 | 0 |
|
254 | (2.4) | 1.5 | 95.6 | 0.28 |
|
746 | (7.2) | 10.0 | 99.1 | 0 |
|
3127 | (30.1 | 0.9 | 99.3 | 0.02 |
|
4291 | (41.8) | 0.4 | 99.2 | 0.03 |
Thyroid | 528 | (5.1) | 0.3 | 99.7 | 0 |
Breast | 307 | (12.7) | – | 99.5 | 0 |
Cervix uteri | 699 | (6.8) | – | 99.4 | 0 |
Colorectum | 357 | (3.5) | 1.1 | 99.4 | 0 |
Other | 610 | (5.9) | 0.7 | 98.5 | 0.1 |
|
160 | (1.6) | 0.7 | 79.5 | 1.6 |
Males | Females | ||||
5-yr RSR (95% CI) | 10-yr RSR (95% CI) | 5-yr RSR (95% CI) | 10-yr RSR (95% CI) | ||
|
15–19 | 0.85 (0.79, 0.90) | 0.80 (0.73, 0.85) | 0.92 (0.85, 0.95) | 0.81 (0.71, 0.88) |
20–29 | 0.89 (0.86, 0.92) | 0.88 (0.84, 0.90) | 0.87 (0.83, 0.90) | 0.79 (0.75, 0.83) | |
30–39 | 0.81 (0.78, 0.83) | 0.75 (0.71, 0.78) | 0.85 (0.83, 0.87) | 0.81 (0.79, 0.82) | |
15–39 | 0.84 (0.82, 0.86) | 0.78 (0.75, 0.81) | 0.86 (0.85, 0.88) | 0.81 (0.78, 0.83) | |
|
15–19 | 0.64 (0.52, 0.86) | 0.53 (0.30, 0.72) | 0.62 (0.49, 0.85) | 0.45 (0.25, 0.67) |
20–29 | 0.73 (0.57, 0.83) | 0.57 (0.33, 0.72) | 0.69 (0.53, 0.94) | 0.62 (0.38, 0.71) | |
30–39 | 0.57 (0.44, 0.82) | 0.54 (0.34, 0.73) | 0.54 (0.34, 0.83) | 0.44 (0.28, 0.63) | |
15–39 | 0.62 (0.46, 0.75) | 0.55 (0.35, 0.67) | 0.61 (0.33, 0.87) | 0.46 (0.36, 0.54) | |
|
15–19 | 0.92 (0.73, 0.99) | 0.93 (0.72, 0.98) | 0.97 (0.94, 0.99) | 0.94 (0.65, 0.99) |
20–29 | 0.89 (0.73, 0.96) | 0.94 (0.82, 0.98) | 0.85 (0.61, 0.95) | 0.83 (0.63, 0.93) | |
30–39 | 0.81 (0.67, 0.88) | 0.71 (0.58, 0.82) | 0.87 (0.72, 0.94) | 0.83 (0.69, 0.91) | |
15–39 | 0.85 (0.77, 0.90) | 0.81 (0.71, 0.88) | 0.84 (0.74, 0.91) | 0.85 (0.74, 0.92) | |
|
15–19 | 0.62 (0.34, 0.89) | 0.47 (0.15, 0.85) | 0.57 (0.23, 0.88) | 0.46 (0.11, 0.80) |
20–29 | 0.66 (0.38, 0.83) | 0.51 (0.29, 0.69) | 0.52 (0.26, 0.73) | 0.33 (0.15, 0.50) | |
30–39 | 0.56 (0.35, 0.78) | 0.35 (0.19, 0.52) | 0.61 (0.26, 0.84) | 0.45 (0.24, 0.65) | |
15–39 | 0.55 (0.41, 0.71) | 0.42 (0.25, 0.57) | 0.52 (0.36, 0.65) | 0.43 (0.22, 0.63) | |
|
15–19 | 0.76 (0.13, 0.97) | 0.67 (0.20, 0.91) | 0.97 (0.94, 1.00) | 0.84 (0.27, 0.98) |
20–29 | 0.87 (0.57, 0.97) | 0.66 (0.38, 0.83) | 0.99 (0.89, 1.00) | 0.72 (0.26, 0.93) | |
30–39 | 0.87 (0.63, 0.96) | 0.65 (0.39, 0.82) | 0.76 (0.51, 0.90) | 0.53 (0.20, 0.78) | |
15–39 | 0.83 (0.67, 0.92) | 0.65 (0.43, 0.81) | 0.81 (0.64, 0.91) | 0.70 (0.38, 0.88) | |
|
15–19 | 0.95 (0.79, 0.99) | 0.94 (0.74, 0.99) | 0.92 (0.72, 0.98) | 0.72 (0.41, 0.89) |
20–29 | 0.99 (0.88, 1.00) | 0.98 (0.92, 1.00) | 0.85 (0.60, 0.97) | 0.80 (0.20, 0.97) | |
30–39 | 0.91 (0.80, 0.96) | 1.00 (0.94, 1.00) | 0.84 (0.27, 0.98) | 0.84 (0.27, 0.97) | |
15–39 | 1.00 (0.97, 1.00) | 0.94 (0.87, 0.97) | 0.87 (0.57, 0.97) | 0.73 (0.46, 0.88) | |
|
15–19 | 0.95 (0.81, 0.99) | 0.95 (0.84, 0.98) | 0.97 (0.93, 0.99) | 0.93 (0.69, 0.99) |
20–29 | 0.97 (0.93, 0.99) | 0.97 (0.93, 0.99) | 0.98 (0.95, 1.00) | 0.98 (0.95, 1.00) | |
30–39 | 0.96 (0.90, 0.97) | 0.93 (0.89, 0.96) | 0.98 (0.95, 0.99) | 0.98 (0.95, 1.00) | |
15–39 | 0.96 (0.92, 0.96) | 0.95 (0.91, 0.97) | 0.98 (0.96, 0.99) | 0.97 (0.94, 0.99) | |
15–19 | 0.88 (0.60, 0.97) | 0.65 (0.38, 0.83) | 0.97 (0.79, 1.00) | 0.75 (0.48, 0.90) | |
|
20–29 | 0.80 (0.70, 0.87) | 0.80 (0.70, 0.88) | 0.86 (0.81, 0.90) | 0.72 (0.64, 0.78) |
30–39 | 0.68 (0.62, 0.73) | 0.60 (0.54, 0.66) | 0.84 (0.81, 0.87) | 0.78 (0.75, 0.81) | |
15–39 | 0.71 (0.67, 0.76) | 0.62 (0.56, 0.68) | 0.85 (0.83, 0.87) | 0.76 (0.73, 0.79) |
Bone Sarcoma not included among subgroup analyses; CI: confidence intervals.
Adjusted excess mortality hazard ratios in AYAs by diagnostic subgroup and calendar period of diagnosis and time since diagnosis are shown in
Calendar period of diagnosisReference: 1985–1989 | Follow-up timeReference: Year 1 | ||||||||
1990–1994 | 1995–1999 | 2000–2004 | Year 2 | Year 3 | Year 4 | Year 5 | |||
|
0.87 (0.44, 0.85) | 0.62 (0.52, 0.73) | 0.52 (0.45, 0.60) | † | 0.64 (0.59, 0.71) | 0.38 (0.34, 0.43) | 0.31 (0.27, 0.35) | 0.22 (0.19, 0.26) | † |
|
1.01 (0.43, 1.37) | 0.94 (0.21, 1.67) | 0.61 (0.38, 1.00) | † | 0.73 (0.54, 0.10) | 0.49 (0.34, 0.72) | 0.36 (0.23, 0.56) | 0.23 (0.13, 0.40) | † |
|
0.87 (0.59, 1.34) | 0.62 (0.43, 0.97) | 0.48 (0.30, 0.84) | ‡ | 0.59 (0.42, 0.83) | 0.35 (0.23, 0.53) | 0.12 (0.06, 0.24) | 0.22 (0.13, 0.37) | † |
|
0.89 (0.47, 1.57) | 1.00 (0.60, 1.71) | 1.04 (0.66, 1.74) | 0.73 (0.53, 0.99) | 0.35 (0.23, 0.54) | 0.40 (0.26, 0.61) | 0.32 (0.19, 0.52) | † | |
|
1.16 (0.70, 2.14) | 0.78 (0.29, 1.90) | 0.62 (0.32, 1.26) | 0.97 (0.62, 1.51) | 0.17 (0.07, 0.41) | 0.29 (0.14, 0.60) | 0.31 (0.15, 0.64) | † | |
|
0.40 (0.26, 0.85) | 0.36 (0.16, 0.95) | 0.21 (0.09, 0.39) | ‡ | 0.70 (0.35, 1.39) | 0.38 (0.16, 0.90) | 0.19 (0.06, 0.64) | 0.05 (0.003, 0.82) | † |
|
0.91 (0.80, 1.37) | 0.37 (0.26, 0.53) | 0.31 (0.12, 0.64) | † | 0.88 (0.55, 1.41) | 0.64 (0.38, 1.09) | 0.71 (0.43, 1.19) | 0.87 (0.54, 1.41) | |
|
0.88 (0.75, 1.04) | 0.78 (0.66, 0.82) | 0.59 (0.50, 0.70) | † | 0.65 (0.58, 0.74) | 0.43 (0.37, 0.50) | 0.35 (0.29, 0.41) | 0.23 (0.18, 0.27) | † |
model also adjusted for sex, Aboriginal status, age at diagnosis, years of follow-up; ARIA, IRSD and Charlson Index; †: highly significant group effect (p<0.0001); ‡: significant group effect (0.001<p<0.05).b Poisson model was not applied to bone sarcomas because of instability and lack of convergence in the regression model.
For all cancers combined and all studied cancer subgroups except melanoma, the risk of dying decreased significantly with duration of time after diagnosis. The annual risk of death from melanoma remained stable throughout 5 years of follow-up (
Considerable differences in the spectrum of cancers experienced by the different age groups were noted (
Sex | Aboriginal | Age at diagnosis, years | Location | Social disadvantage (IRSD) | |||||||||
Reference: Male | Reference: No | Reference: 15–19 | Reference: Urban | Reference: 4th quartile (least disadvantaged) | |||||||||
Female | Yes | 20–29 | 30–39 | Rural/remote | 1st quartile (most) | 2nd quartile | 3rd quartile | ||||||
|
0.66 (0.62, 0.71) | † | 1.47 (1.23, 1.76) | † | 1.07 (0.76, 1.54) | 1.38 (1.21, 1.58) | † | 1.13 (1.04, 1.23) | ‡ | 1.14 (1.04, 1.26) | 1.03 (0.93, 1.13) | 1.01 (0.92, 1.16) | ‡ |
|
1.26 (1.03, 1.63) | ‡ | 1.25 (0.60, 2.63) | 0.98 (0.71, 1.36) | 0.74 (0.52, 1.06) | † | 0.99 (0.70, 1.39) | 1.23 (0.85, 1.76) | 1.18 (0.82, 1.71) | 1.01 (0.71, 1.44) | ‡ | ||
|
0.65 (0.49, 0.87) | ‡ | 1.54 (0.67, 3.56) | 1.30 (0.81, 2.09) | 2.13 (1.39, 3.25) | † | 1.10 (0.80, 1.51) | 1.07 (0.74, 1.55) | 1.29 (0.91, 1.83) | 1.30 (0.91, 1.84) | |||
|
0.92 (0.71, 1.19) | 0.79 (0.36, 1.73) | 1.03 (0.67, 1.57) | 1.60 (1.09, 2.35) | ‡ | 1.09 (0.82, 1.46) | 1.14 (0.82, 1.59) | 1.07 (0.78, 1.49) | 0.97 (0.52, 1.11) | ||||
|
0.54 (0.35, 0.83) | ‡ | 1.28 (0.39, 4.21) | 0.94 (0.52, 1.71) | 1.13 (0.65, 1.95) | 1.21 (0.72, 2.03) | 1.47 (0.83, 2.59) | 1.41 (0.78, 2.57) | 0.92 (0.53, 1.61) | ||||
|
3.71 (2.17, 6.36) | ‡ | 6.69 (2.18, 20.6) | † | 0.97 (0.69, 1.80) | 1.03 (0.61, 1.89) | 1.08 (0.58, 2.01) | 0.83 (0.42, 1.65) | 1.59 (0.84, 3.07) | 1.14 (0.55, 2.34) | |||
|
0.53 (0.38, 0.74) | † | 3.63 (0.50, 26.2) | 1.19 (0.67,.10) | 1.28 (0.74, 2.21) | 1.09 (0.83, 1.43) | 1.21 (0.87, 1.69) | 1.09 (0.79, 1.50) | 1.08 (0.79, 1.47) | ||||
|
0.51 (0.46, 0.56) | † | 1.25 (1.01, 1.54) | ‡ | 1.25 (0.88, 1.76) | 1.80 (1.30, 2.50) | † | 1.38 (1.24, 1.82) | † | 0.99 (0.87, 1.12) | 1.07 (0.94, 1.21) | 0.98 (0.86, 1.12) |
model also adjusted for years of follow-up, calendar period and Charlson Index; †: highly significant group effect (p<0.0001); ‡: significant group effect (0.001<p<0.05).
Survival for all cancers combined was poorer in males compared with females (0.66, 0.62–0.71): females with lymphoma (0.65, 0.49–0.87), soft tissue sarcoma (0.54, 0.35–0.83), melanoma (0.53, 0.38–0.74) and carcinomas (0.51, 0.46–0.56) experienced significantly lower excess mortality compared with males (
Non-Aboriginal AYAs comprised the majority (n = 9445, 92%) of cancer cases. After adjusting for sex, age at diagnosis, co-morbidity, locational disadvantage, SES, length of follow-up and year of diagnosis, Aboriginality significantly contributed to the risk of death. Overall, Aboriginal AYAs experienced a higher excess mortality (1.47, 1.23–1.76) compared with non-Aboriginal AYAs. Among the diagnostic subgroups, Aboriginal AYAs diagnosed with carcinomas experienced 25% significantly greater excess mortality (1.25, 1.01–1.54) than their non-Aboriginal counterparts; and those diagnosed with germ cell tumours experienced nearly seven times higher excess mortality (6.7, 2.2–20.6). The results for the other subgroups did not reach statistical significance.
AYAs living in rural and remote areas had an increased risk of mortality compared with those who lived in urban areas (1.13, 1.04–1.23). However, among the diagnostic subgroups, the risk was only significant for those diagnosed with carcinomas (1.38, 1.24–1.82). A significant gradient of increased mortality with declining SES for all cancers combined was observed (HR, lowest versus highest quartile: 1.14, trend p<0.05). Among the cancer subgroups, the adjusted HRs were only significant for AYAs diagnosed with leukaemias (trend p<0.001). Approximately 65% of Aboriginal AYAs (versus 23% non-Aboriginal) resided in rural and remote areas. Aboriginal AYAs in WA were over-represented in the most socially disadvantaged categories, with less than 5% of Aboriginal AYAs cancer patients (versus 27% non-Aboriginal) in the highest quartile (least disadvantaged group).
This paper reports estimates of long-term relative survival and risk of excess mortality in AYAs diagnosed with cancer in WA, using the current SEER classification for AYAs cancer subgroups. Recently treated AYAs had a significantly lower risk of death than those treated earlier in the study period. Older age at of diagnosis was a predictor of poor prognosis for all cancers combined and for lymphoma, CNS tumours and carcinomas indvidually. In general, female AYAs had better survival outcomes compared with their male counterparts. Aboriginality was identified as a poor prognostic factor, particularly among AYAs diagnosed with germ cells tumours. Our results reinforce the importance of both socio-economic status and area of residence in the survival of AYAs diagnosed with cancers.
Recently diagnosed AYAs (2000–2004) in this population were estimated to have 50% lower excess mortality compared with those diagnosed in 1980s. In particular, survival from leukaemias, lymphomas, germ cell tumours, melanoma and carcinomas has improved markedly over the last few decades. This is likely to reflect the increasing availability of better diagnostic techniques and more effective therapies. Male germ cell tumours and melanomas presented the best prognoses with 5-year RSRs of around 0.95 and 1.00, respectively. In a previous study, significant improvements in outcomes from the treatment of germ cell tumours had occurred mainly before the 1980s, coincident with the widespread introduction of platinum-based treatment regimens
Of concern are the poor outcomes associated with CNS tumours in this study. It is difficult to ascertain the reasons for these. Unfortunately, WA Cancer Registry data on CNS tumours do not include important predictors of survival, such as histological categorisation (in a substantial minority of cases), extent of disease, and molecular typing such as
The association of older age at diagnosis with poorer long-term survival among AYAs diagnosed with lymphoma, CNS and carcinomas is concordant with previous reports for this age group. By contrast, in the case of leukaemia, survival was worse among younger AYAs, aged 15–29 years compared with those older than 30 years. Past research has shown similar disparities, with children and adolescents diagnosed with leukemia. Unlike children with biologically similar leukaemias, younger AYAs are often administered adult rather than paediatric treatment regimens, which may ultimately be less effective
Male AYAs diagnosed with germ cell tumours had markedly reduced excess mortality compared with their female counterparts had better survival outcomes than females, whereas males diagnosed with lymphoma, soft tissue sarcoma, melanoma and carcinomas had a worse prognosis. Sex did not significantly affect prognosis in those diagnosed with CNS tumours, which is consistent with previous studies
AYAs with cancer resident in rural and remote areas at the time of diagnosis had an increased risk of mortality compared with their urban-dwelling counterparts. Stratified analyses by diagnostic group indicated a socioeconomic gradient in survival in those diagnosed with carcinomas. The observed survival disparity for carcinoma may reflect restricted access to optimal care and low density of health care facilities in rural and remote Australia. Previous studies in WA have indeed identified a service delivery gap for rural residents diagnosed with carcinomas of the colon and rectum
Our study is the first to examine socioeconomic impacts on survival in Australian AYAs with cancer, demonstrating higher excess mortality in AYAs living in socio-economically disadvantaged groups, regardless of area of residence. The effects of SES on prognosis for AYAs were significant for all cancers combined. However, when groups were analysed individually, a significant gradient was detected only for leukaemias. A similar pattern for leukaemias has been found in another study
Our study also revealed that young Aboriginal people diagnosed with cancer experienced worse survival outcomes compared with non-Aboriginal AYAs. Notably, Aboriginal AYAs diagnosed with germ cell tumours experienced nearly seven-times greater mortality compared with non-Aboriginal AYAs. Although, there is risk of important residual confounding by location due to our stratified analysis of locational disadvantage (urban vs. rural & remote), the difference in survival by Aboriginality persisted after adjustment for area-based SES and locational disadvantage). This would suggest the possibility of important biological or other unknown factors that contribute to worse survival in Aboriginal males. However, it is difficult from our analysis alone to determine whether differences were due to biology or other possible factors such as differential access to adequate treatment or and health behaviours.
A major strength of this study is its use of routinely-collected, whole-population data from the WADLS, which has undergone extensive validation, with false-positives and false-negatives of subject identification shown to be <1%. Rigorous characterisation of the cancers and careful follow-up were important features of the WACR. Rigorous procedures are implemented to ensure that cancer ascertainment is as complete as possible; cases are identified from multiple sources. In relation to the main indicator of data quality, namely, the modality of diagnosis (microscopic confirmation or death certificate only)
Given the relative homogeneity of the Australian health system, and that WA is socio-demographically representative of Australia as a whole, these results may be considered to reflect AYA cancer outcomes nationally
A major limitation of the present study was the lack of cancer staging data. We were unable to make a more in-depth assessment of differences in survival by stage at diagnosis because this information is not routinely collected by the WACR. Additional limitations include the potential for residual socio-economic confounding, as estimation of SES was based on the locality of residence at diagnosis, with measurement at the individual level not possible from de-identified linked data. Our approach may not have accurately captured some factors that contribute to cancer survival, such as healthy living environments, and adequate medical care. Additionally, AYAs are a heterogeneous group by virtue of transitioning through developmental life stages: some are dependent on parents and relatives while others provide for families of their own. As such, measuring SES as a single point-in-time geographical area composite variable may inadequately summarize an individual patient's life-course social and financial circumstances. On the other hand, only focusing on individuals ignores the broader issues of area contextual effects on health, such as community resources for healthy living.
An RSR estimate reflects the ratio of the observed survival divided by the expected survival of a cohort of the general population possessing similar characteristics with respect to age at calendar time/era and sex. Other major advantages of working with RSR estimates include the fact that data on cause of death are not required, which circumvents difficulties with inaccuracy or lack of death certification. However, this apparent strength may present important limitations for our AYA cohort. Young adults aged 20–39 years are the age group at the highest risk of acquiring HIV infection
Survival of AYAs diagnosed with cancer has generally increased over time. Despite favourable survival prognoses for some cancers in AYAs, there remains considerable disparity in cancer outcomes between different socio-demographic categories of AYA patients as well as substantial variation in the outcomes from different categories of cancer. Survival differentials identified in this study, particularly in relation to testicular germ cell tumours, should be investigated in greater depth; in order to distinguish instances in which improvements in survival can be attained through promoting equity of service access from those requiring novel therapeutic strategies directed towards distinctive aspects of tumour biology.
The authors thank the Western Australian Data Linkage Branch for their assistance and provision of data.