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OBJECTIVES: To estimate the incidence of melanoma in Australia among people with ancestries associated with low, moderate, or high risk of melanoma, by sex and 5-year age group; to establish whether age-specific incidence rates by ancestry risk group have changed over time. STUDY DESIGN: Modelling study; United States (SEER database) melanoma incidence rates for representative ancestral populations and Australian census data (2006, 2011, 2016, 2021) used to estimate Australian melanoma incidence rates by ancestry-based risk. SETTING, PARTICIPANTS: Australia, 2006-2021. MAIN OUTCOME MEASURES: Age-specific invasive melanoma incidence rates, and average annual percentage change (AAPC) in age-specific melanoma rates, by ancestry-based risk group, sex, and 5-year age group. RESULTS: The proportion of people in Australia who reported high risk (European) ancestry declined from 85.3% in 2006 to 71.1% in 2021. The estimated age-standardised melanoma incidence rate was higher for people with high risk ancestry (2021: males, 82.2 [95% confidence interval {CI}, 80.5-83.8] cases per 100 000 population; females, 58.5 [95% CI, 57.0-59.9] cases per 100 000 population) than for all Australians (males, 67.8 [95% CI, 66.5-69.2] cases per 100 000 population; females, 45.4 [95% CI, 44.3-46.5] cases per 100 000 population). AAPCs were consistently positive for Australians aged 50 years or older, both overall and for people with high risk ancestry, but were statistically significant only for some age groups beyond 65 years. AAPCs were negative for people aged 34 years or younger, but were generally not statistically significant. CONCLUSIONS: Melanoma incidence has declined in some younger age groups in Australia, including among people with high risk ancestry. Social and behavioural changes over the same period that lead to lower levels of ultraviolet radiation exposure probably contributed to these changes.

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OBJECTIVE: The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences. MATERIALS AND METHODS: The preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group. RESULTS: The initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency. DISCUSSION: We reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases. CONCLUSION: The ACA has been hugely successful since launching in 2018. It has received over 62 000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects.

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OBJECTIVE: Improvement in cancer survival over recent decades has not been accompanied by a narrowing of socioeconomic disparities. This study aimed to quantify the loss of life expectancy (LOLE) resulting from a cancer diagnosis and examine disparities in LOLE based on area-level socioeconomic status (SES). METHODS: Data were collected for all people between 50 and 89 years of age who were diagnosed with cancer, registered in the NSW Cancer Registry between 2001 and 2019, and underwent mortality follow-up evaluations until December 2020. Flexible parametric survival models were fitted to estimate the LOLE by gender and area-level SES for 12 common cancers. RESULTS: Of 422,680 people with cancer, 24% and 18% lived in the most and least disadvantaged areas, respectively. Patients from the most disadvantaged areas had a significantly greater average LOLE than patients from the least disadvantaged areas for cancers with high survival rates, including prostate [2.9 years (95% CI: 2.5-3.2 years) vs. 1.6 years (95% CI: 1.3-1.9 years)] and breast cancer [1.6 years (95% CI: 1.4-1.8 years) vs. 1.2 years (95% CI: 1.0-1.4 years)]. The highest average LOLE occurred in males residing in the most disadvantaged areas with pancreatic [16.5 years (95% CI: 16.1-16.8 years) vs. 16.2 years (95% CI: 15.7-16.7 years)] and liver cancer [15.5 years (95% CI: 15.0-16.0 years) vs. 14.7 years (95% CI: 14.0-15.5 years)]. Females residing in the least disadvantaged areas with thyroid cancer [0.9 years (95% CI: 0.4-1.4 years) vs. 0.6 years (95% CI: 0.2-1.0 years)] or melanoma [0.9 years (95% CI: 0.8-1.1 years) vs. 0.7 years (95% CI: 0.5-0.8 years)] had the lowest average LOLE. CONCLUSIONS: Patients from the most disadvantaged areas had the highest LOLE with SES-based differences greatest for patients diagnosed with cancer at an early stage or cancers with higher survival rates, suggesting the need to prioritise early detection and reduce treatment-related barriers and survivorship challenges to improve life expectancy.

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BACKGROUND: Event-free survival (EFS) considers other adverse events in addition to mortality. It therefore provides a more complete understanding of the effectiveness and consequences of treatment than standard survival measures, but is rarely reported at the population level for childhood cancer. PROCEDURE: Our study cohort (n = 7067) was obtained from the Australian Childhood Cancer Registry, including children aged under 15 diagnosed with cancer between 2006 and 2015, with follow-up potentially available to 31 December 2020. The events of interest were relapse following remission, progressive disease, diagnosis of a second primary cancer or death from any cause. Five-year EFS and all-cause observed survival were both calculated, stratified by type of childhood cancer, remoteness of residence and stage at diagnosis. Differences in EFS were assessed using multivariable flexible parametric models. RESULTS: Approximately one quarter of patients (n = 1605 of 7067, 23%) experienced at least one of the events of interest within 5 years of diagnosis. Relapse was twice as common for children with metastatic/advanced disease (22%) versus children with localised/limited cancers (11%). Overall 5-year EFS was 75.0% (95% confidence interval [CI]: 73.9%-76.0%), compared to 85.8% observed survival (95% CI: 85.0%-86.6%). Patients with other gliomas had the lowest EFS (35.4%, 95% CI: 27.8%-43.1%). EFS was significantly lower among children with acute myeloid leukaemia in outer regional/remote areas compared to major cities (adjusted hazard ratio [HR] = 1.90, 95% CI: 1.20-3.00). CONCLUSIONS: Reporting EFS at a population level provides further insight on a wider range of impacts apart from mortality alone, contributing towards efforts to improve the management and outcomes of childhood cancer.

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This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.

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OBJECTIVE: This article aims to examine cross-sectional associations and assess temporal trends in keratinocyte carcinoma (KC) incidence by area-level socioeconomic status (SES) and geographic remoteness in Tasmania, Australia. METHODS: KCs - basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC) - registered by the Tasmanian Cancer Registry were assigned to area-level SES and remoteness area. Incidence rate ratios (2014-2018) were estimated using Poisson regression. Average annual percentage changes (2001-2018) were estimated using the Joinpoint Regression Program. RESULTS: BCC incidence increased with increasing area-level advantage (p-value for trend <0.001), but no trend was found for SCC. SCC incidence was higher in rural than urban areas (p-value <0.001), and BCC incidence was slightly higher in rural than urban areas for females (p-value = 0.009), but not for males (p-value = 0.373). BCC and SCC incidence increased between 2001 and the mid-2010s, when it peaked across most areas. CONCLUSIONS: Associations were found between BCC and higher area-level SES, and between SCC and geographic remoteness. The findings suggest differences in sun exposure behaviours, skin cancer awareness and access to services, or ascertainment bias. IMPLICATIONS FOR PUBLIC HEALTH: Efforts to control and deliver KC services in Tasmania should consider targeting populations with specific area-level characteristics.

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BACKGROUND: Delays to breast cancer treatment can lead to more aggressive and extensive treatments, increased expenses, increased psychological distress, and poorer survival. We explored the individual and area level factors associated with the interval between diagnosis and first treatment in a population-based cohort in Queensland, Australia. METHODS: Data from 3216 Queensland women aged 20 to 79, diagnosed with invasive breast cancer (ICD-O-3 C50) between March 2010 and June 2013 were analysed. Diagnostic dates were sourced from the Queensland Cancer Registry and treatment dates were collected via self-report. Diagnostics-treatment intervals were modelled using flexible parametric survival methods. RESULTS: The median interval between breast cancer diagnosis and first treatment was 15 days, with an interquartile range of 9-26 days. Longer diagnostic-treatment intervals were associated with a lack of private health coverage, lower pre-diagnostic income, first treatments other than breast conserving surgery, and residence outside a major city. The model explained a modest 13.7% of the variance in the diagnostic-treatment interval [Formula: see text]. Sauerbrei's D was 0.82, demonstrating low to moderate discrimination performance. CONCLUSION: Whilst this study identified several individual- and area-level factors associated with the time between breast cancer diagnosis and first treatment, much of the variation remained unexplained. Increased socioeconomic disadvantage appears to predict longer diagnostic-treatment intervals. Though some of the differences are small, many of the same factors have also been linked to screening and diagnostic delay. Given the potential for accumulation of delay at multiple stages along the diagnostic and treatment pathway, identifying and applying effective strategies address barriers to timely health care faced by socioeconomically disadvantaged women remains a priority.

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BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

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BACKGROUND: Treatment decisions for men diagnosed with prostate cancer depend on a range of clinical and patient characteristics such as disease stage, age, general health, risk of side effects and access. Associations between treatment patterns and area-level factors such as remoteness and socioeconomic disadvantage have been observed in many countries. OBJECTIVE: To model spatial differences in interventional treatment rates for prostate cancer at high spatial resolution to inform policy and decision-making. METHODS: Hospital separations data for interventional treatments for prostate cancer (radical prostatectomy, low dose rate and high dose rate brachytherapy) for men aged 40 years and over were modelled using spatial models, generalised linear mixed models, maximised excess events tests and k-means statistical clustering. RESULTS: Geographic differences in population rates of interventional treatments were found (p<0.001). Separation rates for radical prostatectomy were lower in remote areas (12.2 per 10 000 person-years compared with 15.0-15.9 in regional and major city areas). Rates for all treatments decreased with increasing socioeconomic disadvantage (radical prostatectomy 19.1 /10 000 person-years in the most advantaged areas compared with 12.9 in the most disadvantaged areas). Three groups of similar areas were identified: those with higher rates of radical prostatectomy, those with higher rates of low dose brachytherapy, and those with low interventional treatment rates but higher rates of excess deaths. The most disadvantaged areas and remote areas tended to be in the latter group. CONCLUSIONS: The geographic differences in treatment rates may partly reflect differences in patients' physical and financial access to treatments. Treatment rates also depend on diagnosis rates and thus reflect variation in investigation rates for prostate cancer and presentation of disease. Spatial variation in interventional treatments may aid identification of areas of under-treatment or over-treatment.

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OBJECTIVES: To assess associations between breast cancer-specific survival and timeliness of treatment, based on 2020 Australian guidelines for the treatment of early breast cancer. DESIGN: Population-based cohort study; analysis of linked Queensland Cancer Register, patient medical record, and National Death Index data, supplemented by telephone interviews. SETTING, PARTICIPANTS: Women aged 20-79 years diagnosed with invasive breast cancer during 1 March 2010 - 30 June 2013, followed to 31 December 2020. MAIN OUTCOME MEASURES: Breast cancer-specific survival for women who received or did not receive treatment within the recommended timeframe, overall and for six treatment intervals; optimal cut-points for each treatment interval; characteristics of women for whom treatment was not provided within the recommended timeframe. RESULTS: Of 5426 eligible women, 4762 could be invited for interviews; complete data were available for 3044 women (56% of eligible women, 65% of invited women). Incomplete compliance with guideline interval recommendations was identified for 1375 women (45%); their risk of death from breast cancer during the follow-up period was greater than for those for whom guideline compliance was complete (adjusted hazard ratio [aHR], 1.43; 95% confidence interval [CI], 1.04-1.96). Risk of death was greater for women for whom the diagnosis to surgery interval exceeded 29 days (aHR, 1.76; 95% CI, 1.19-2.59), the surgery to chemotherapy interval exceeded 36 days (aHR, 1.63; 95% CI, 1.13-2.36), or the chemotherapy to radiotherapy interval exceeded 31 days (aHR, 1.83; 95% CI, 1.19-2.80). Treatment intervals longer than recommended were more frequent for women for whom breast cancer was detected by public facility screening (adjusted odds ratio [aOR], 1.58; 95% CI, 1.22-2.04) or by symptoms (aOR, 1.39; 95% CI, 1.09-1.79) than when cancer had been detected in private facilities, and for women without private health insurance (aOR, 1.96; 95% CI, 1.66-2.32) or living outside major cities (aOR, 1.38; 95% CI, 1.18-1.62). CONCLUSIONS: Breast cancer-specific survival was poorer for women for whom the diagnosis to surgery, surgery to chemotherapy, or chemotherapy to radiotherapy intervals exceeded guideline-recommended limits. Our findings support 2020 Australian guideline recommendations regarding timely care.

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BACKGROUND: The Toronto Paediatric Cancer Stage Guidelines are a compendium of staging systems developed to facilitate collection of consistent and comparable data on stage at diagnosis for childhood cancers by cancer registries. MATERIAL AND METHODS: This retrospective, observational cohort study investigated changes in stage-specific incidence and survival for children diagnosed between 2000-2008 compared to 2009-2017 using the population-based Australian Childhood Cancer Registry. Information on mortality for each patient was available to 31st December 2020. Shifts in incidence by stage were evaluated using chi-square tests, and differences in stage-specific five-year observed survival for all causes of death over time were assessed using flexible parametric models. RESULTS: Stage was assigned according to the Toronto Guidelines for 96% (n = 7944) of the total study cohort (n = 8292). Changes in the distribution of incidence by stage between the two diagnosis periods were observed for retinoblastoma, with stage 0 increasing from 26% to 37% of cases (p = 0.02), and hepatoblastoma, with metastatic disease increasing from 22% to 39% of cases (p = 0.04). There were large gains in stage-specific survival over time for stage IV rhabdomyosarcoma (five-year adjusted mortality hazard ratio for 2009-2017 compared to 2000-2008 of 0.38, 95% CI 0.19-0.77; p = 0.01), stage M3 for medulloblastoma (HR = 0.41, 95% CI 0.21-0.79; p = 0.01) and metastatic neuroblastoma excluding stage MS (HR = 0.61, 95% CI 0.44-0.84; p < 0.01). CONCLUSION: These results indicate that improvements in childhood cancer survival in Australia are most likely due to refined management rather than changes in stage at diagnosis, particularly for metastatic solid tumours. Wide international uptake of the Toronto Guidelines will allow comprehensive evaluation of differences in survival between countries.

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BACKGROUND: Participation in bowel cancer screening programs remains poor in many countries. Knowledge of geographical variation in participation rates may help design targeted interventions to improve uptake. This study describes small-area and broad geographical patterns in bowel screening participation in Australia between 2015-2020. METHODS: Publicly available population-level participation data for Australia's National Bowel Cancer Screening Program (NBCSP) were modelled using generalized linear models to quantify screening patterns by remoteness and area-level disadvantage. Bayesian spatial models were used to obtain smoothed estimates of participation across 2,247 small areas during 2019-2020 compared to the national average, and during 2015-2016 and 2017-2018 for comparison. Spatial heterogeneity was assessed using the maximized excess events test. RESULTS: Overall, screening participation rates was around 44% over the three time-periods. Participation was consistently lower in remote or disadvantaged areas, although heterogeneity was evident within these broad categories. There was strong evidence of spatial differences in participation over all three periods, with little change in patterns between time periods. If the spatial variation was reduced (so low participation areas were increased to the 80th centile), an extra 250,000 screens (4% of total) would have been conducted during 2019-2020. CONCLUSIONS: Despite having a well-structured evidence-based government funded national bowel cancer screening program, the substantial spatial variation in participation rates highlights the importance of accounting for the unique characteristics of specific geographical regions and their inhabitants. Identifying the reasons for geographical disparities could inform interventions to achieve more equitable access and a higher overall bowel screening uptake.

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OBJECTIVE: This article aims to examine cross-sectional associations and assess temporal trends in keratinocyte carcinoma (KC) incidence by area-level socioeconomic status (SES) and geographic remoteness in Tasmania, Australia. METHODS: KCs-basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC)-registered by the Tasmanian Cancer Registry were assigned to area-level SES and remoteness area. Incidence rate ratios (2014-2018) were estimated using Poisson regression. Average annual percentage changes (2001-2018) were estimated using the Joinpoint Regression Program. RESULTS: BCC incidence increased with increasing area-level advantage (p value for trend <0.001), but no trend was found for SCC. SCC incidence was higher in rural than urban areas (p value <0.001), and BCC incidence was slightly lower in rural than urban areas for males (p value = 0.026), but not for females (p value = 0.381). BCC and SCC incidence increased between 2001 and the mid-2010s, when it peaked across most areas. CONCLUSIONS: Associations were found between BCC and higher area-level SES, and between SCC and geographic remoteness. The findings suggest differences in sun exposure behaviours, skin cancer awareness and access to services, or ascertainment bias. IMPLICATIONS FOR PUBLIC HEALTH: Efforts to control and deliver KC services in Tasmania should consider targeting populations with specific area-level characteristics.

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Spatial modeling of cancer survival is an important tool for identifying geographic disparities and providing an evidence base for resource allocation. Many different approaches have attempted to understand how survival varies geographically. This is the first scoping review to describe different methods and visualization techniques and to assess temporal trends in publications. The review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline using PubMed and Web of Science databases. Two authors independently screened articles. Articles were eligible for review if they measured cancer survival outcomes in small geographical areas by using spatial regression and/or mapping. Thirty-two articles were included, and the number increased over time. Most articles have been conducted in high-income countries using cancer registry databases. Eight different methods of modeling spatial survival were identified, and there were seven different ways of visualizing the results. Increasing the use of spatial modeling through enhanced data availability and knowledge sharing could help inform and motivate efforts to improve cancer outcomes and reduce excess deaths due to geographical inequalities. Efforts to improve the coverage and completeness of population-based cancer registries should continue to be a priority, in addition to encouraging the open sharing of relevant statistical programming syntax and international collaborations.

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BACKGROUND: While it is known that national PSA testing rates have decreased in Australia since 2007, it is not known whether these trends are consistent by broad geographical areas, nor whether previously reported area-specific differences have remained in more recent time periods. METHODS: Population-based cohort study of Australian men (n = 2793,882) aged 50-69 who received at least one PSA test (Medicare Benefit Schedule item number 66655) during 2002-2018. Outcome measures included age-standardised participation rate, annual percentage change using JoinPoint regression and indirectly standardised participation rate ratio using multivariable Poisson regression. RESULTS: During 2005-09, two thirds (68%) of Australian men aged 50-69 had at least one PSA test, reducing to about half (48%) during 2014-18. In both periods, testing rates were highest among men living in major cities, men aged 50-59 years, and among men living in the most advantaged areas. Nationally, the Australian PSA testing rate increased by 9.2% per year between 2002 and 2007, but then decreased by 5.0% per year to 2018. This pattern was generally consistent across States and Territories, and socio-economic areas, however the magnitude of the trends was less pronounced in remote and very remote areas. CONCLUSIONS: The decreasing trends are consistent with a greater awareness of the current guidelines for clinical practice in Australia, which recommend a PSA test be done only with the informed consent of individual men who understand the potential benefits and risks. However, given there remain substantial geographical disparities in prostate cancer incidence and survival in Australia, along with the equivocal evidence for any benefit from PSA screening, there remains a need for more effective diagnostic strategies for prostate cancer to be implemented consistently regardless of where men live.

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BACKGROUND: Interval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC. METHODS: Telephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010-2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation. RESULTS: Compared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9-4.3), high-grade (OR = 2.36, 1.9-2.9) and triple-negative cancers (OR = 2.55, 1.9-3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6-0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2-2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1-1.7), received hormone replacement therapy (2-10 years: OR = 1.33, 1.0-1.7; > 10 years: OR = 1.55, 1.1-2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2-2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2-2.0). CONCLUSION: These results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.

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BACKGROUND: Large improvements in childhood cancer survival have been reported over recent decades. Data from cancer registries have the advantage of providing a 'whole of population' approach to gauge the success of cancer control efforts. OBJECTIVES: The aim of this study was to investigate recent survival estimates for children diagnosed with cancer Australia and to examine the extent of changes in survival over the last 35 years. For the first time, we also estimated the number of deaths among Australian children that were potentially avoided due to improvements in survival. METHODS: A retrospective, population-based cohort study design was used. Case information was extracted from the Australian Childhood Cancer Registry for 1983-2016, with follow-up to 31 December 2017. Eligible children were aged 0-14 with a basis of diagnosis other than autopsy or death certificate only. Five-year relative survival was calculated using the semi-complete cohort method for three diagnosis periods (1983-1994, 1995-2006 and 2007-2016), and changes in survival over time were assessed via flexible parametric models. Avoided deaths within 5 years for those diagnosed between 1995 and 2016 were estimated under the assumption that survival rates remained the same as for 1983-1994. RESULTS: Overall 5-year survival within the study cohort (n = 20,871) increased from 72.8% between 1983 and1994 to 86.1% between 2007 and 2016, equating to an adjusted excess mortality hazard ratio of 1.82 (95% confidence interval 1.67, 1.97). Most cancers showed improvements in survival; other gliomas, hepatoblastoma and osteosarcoma were exceptions. Among children diagnosed between 1995 and 2016, 38.7% of expected deaths within 5 years of diagnosis (n = 1537 of 3970) were avoided due to temporal improvements in survival. CONCLUSIONS: Survival for childhood cancer has continued to improve over recent years, thanks mainly to ongoing progress in treatment development combined with improved supportive care. Providing innovative measures of survival, such as avoided deaths, may assist with understanding outcome data produced by cancer registries.

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Estimates of childhood cancer survival are usually reported at 5 years after diagnosis only. Using cases prevalent between 2014 and 2018 from the population-based Australian Childhood Cancer Registry, we used the period method to calculate relative survival up to 20 years post diagnosis by cancer type. Twenty-year relative survival for all childhood cancers combined (n = 14,353) was 83.8% (95% confidence interval [CI] = 82.6%-85.0%). Survival decreased only slightly after 10 years for most childhood cancers, except for some types of brain and liver tumours. These contemporary estimates of long-term survival provide valuable information to assist childhood cancer patients and their families in planning for the future.

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Rare cancers collectively account for around a quarter of cancer diagnoses and deaths. However, epidemiological studies are sparse. We describe spatial and geographical patterns in incidence and survival of rare cancers across Australia using a population-based cancer registry cohort of rare cancer cases diagnosed among Australians aged at least 15 years, 2007 to 2016. Rare cancers were defined using site- and histology-based categories from the European RARECARE study, as individual cancer types having crude annual incidence rates of less than 6/100 000. Incidence and survival patterns were modelled with generalised linear and Bayesian spatial Leroux models. Spatial heterogeneity was tested using the maximised excess events test. Rare cancers (n = 268 070) collectively comprised 22% of all invasive cancer diagnoses and accounted for 27% of all cancer-related deaths in Australia, 2007 to 2016 with an overall 5-year relative survival of around 53%. Males and those living in more remote or more disadvantaged areas had higher incidence but lower survival. There was substantial evidence for spatial variation in both incidence and survival for rare cancers between small geographical areas across Australia, with similar patterns so that those areas with higher incidence tended to have lower survival. Rare cancers are a substantial health burden in Australia. Our study has highlighted the need to better understand the higher burden of these cancers in rural and disadvantaged regions where the logistical challenges in their diagnosis, treatment and support are magnified.

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