BACKGROUND:Due to the difficulty of obtaining population-based individual-level data, ecological studies are often used to explore factors related to geographic variations in health outcomes. This study proposes a novel framework to identify area-level predictors of spatial variations in lung cancer outcomes and generate a lung cancer vulnerability index (LcVI) based on these predictors. METHODS:Data on 11,313 persons diagnosed with invasive lung cancer in Queensland, Australia (2016-2019) were sourced from the population-based Queensland Cancer Register. Bayesian spatial models estimated smoothed standardised incidence ratios (SIRs) for 519 geographic areas. Area-level variables (n = 911) were extracted from multiple data collections. Random forest models were fitted to identify important predictors for lung cancer incidence rates. A novel non-parametric dimensionality reduction approach incorporating the final random forest model results was developed to generate the LcVI which ranged from 0-10. RESULTS:Eight variables were identified as predictors for lung cancer incidence with the top two being the prevalence of diabetes and adequate fruit intake. Areas having incidence rates below the Queensland average had significantly lower LcVI than those with average incidence rates (mean difference = 2.80, 95% CI: 2.34-3.25, p < 0.001) while areas with above average incidence rates had significantly higher LcVI than those with average incidence (mean difference = 2.70, 95% CI: 2.20-3.19, p < 0.001). The LcVI was strongly associated with the continuous SIR, explaining 57% of the variation (R² = 0.57, p < 0.001). CONCLUSION:This novel approach identified a small number of important predictors for lung cancer incidence from a high-dimensional dataset. The lung cancer vulnerability index partially explained the geographic variations, potentially offering insights into underlying drivers. As an ecological analysis, this associations reflect relationships at the population level. Future research incorporating individual-level data is needed to confirm whether the area-level associations observed here hold true for individuals.

BACKGROUND:Comorbidities are common among women with breast cancer, yet their independent contribution to prognosis remains poorly characterised, particularly across disease stages. Most prognostic models prioritise tumour-related factors and when comorbidities are included, they are often summarised using aggregate indices, limiting evaluation of specific conditions. We investigated whether individual comorbidities exert effects on breast cancer-specific survival stratified by stage. METHODS:We analysed 3323 women aged 20 - 79 years diagnosed with invasive breast cancer in Queensland, Australia (2010-2013) with a follow-up to December 2020. Comorbidities were self-reported and mapped to ICD-10. Flexible parametric survival models were fitted separately for early (I-II) and late (III-IV) stages, adjusting for established clinical factors. Prognostic performance was quantified using Royston's D-statistic, RD2 and Harrell's concordance index. RESULTS:Comorbidities had no prognostic impact on early-stage disease. In late-stage breast cancer, chronic bronchitis independently predicted poorer breast cancer-specific survival after adjusting for subtype, grade, age, family history and detection mode. The final late-stage model demonstrated good discrimination (C-statistic 0.71) and explained 26% of the variation in survival. Model-based predictions showed clinically meaningful reductions in 5- and 7-year survival among women with bronchitis, partially mitigated by a family history of breast/ovarian cancer. CONCLUSION:Stage stratification revealed prognostic signals that were obscured in pooled analyses. Chronic bronchitis worsened survival in late-stage disease, highlighting the value of assessing individual comorbidities during treatment planning. These findings support guideline recommendations for comorbidity informed management and underscore the need for stage-specific prognostic modelling frameworks.

Background: Incremental improvements in early detection, diagnosis and treatment of cancer have led to an increasing number of cancer survivors worldwide. Existing cancer prevalence statistics however have seldom focused on specific phase-of-care pathways essential to inform cancer survivors’ and healthcare needs. Methods: We estimated 5-year cancer prevalence in 186 countries in 2024 using available estimates of cancer incidence and survival by cancer type, time since diagnosis, sex for ages at diagnosis > 15 years. We defined three clinically distinct phases of care: (a) the initial phase of treatment; (b) the phase from initial treatment to end-of-life care where patients are followed-up; and (c) the “end-of-life” phase. These were calculated by first partitioning the 1-year survival to either treatment (if the patient was alive), or end-of-life (if the patient died). For the remaining four years, we then assigned cases to the follow-up phase if they were alive, or end-of-life, if they died from cancer or other causes. Results: The 5-year prevalence was estimated to be approximately 52.4 million, indicating 1% of the global adult population were living within five years of a diagnosis in 2024. Around 22.9% (12/52 million) of prevalent cases were in diagnosis and treatment, 65.5% (34/52 million) in follow-up and 11.6% (6/52 million) were at the end-of-life phases. Female breast, colorectal and prostate cancers were the most prevalent cancer types contributing 43% (23/52 million) of the total prevalent cases, with almost three-quarters of the prevalent cases in the follow-up phase. On the other hand, lung cancer, the fourth most prevalent cancer, had a larger proportion of prevalent cases (4.1 million) in the end-of-life phase (32%, 1.3/4.1 million). Cancer prevalence as a proportion was higher in very high versus low Human Development Index (HDI) countries (2,608 vs. 417 survivors per 100,000 respectively), yet the proportion in the end-of-life phase was higher among low HDI countries (8% vs. 12% of all prevalent cases in very high vs low HDI, respectively). Interpretation: Among the 52 million cancer survivors estimated in 2024, there are substantial variations in cancer survivorship at different phases of care, driven by distinct cancer profiles coupled with survival disparities. This study highlights the need for equitable access to early detection, timely diagnosis and comprehensive care to improve survival and quality of life cancer survivors worldwide.

OBJECTIVE:Disparities in colorectal cancer exist for incidence and survival in disadvantaged and rural areas. This study created a composite indicator of multiple modifiable risk factors for colorectal cancer to visualise relative prevalence across Australia at the small-area level and by socio-economic status. DESIGN:Cross-sectional ecological analysis. SETTING:The Australian 2020-2021 National Health Survey. PARTICIPANTS:9796 participants sampled from the Australian adult population (18 years +), living in 2350 small areas. METHODS:Data on the prevalence of smoking, alcohol consumption, body mass index, and physical activity were used to generate colorectal cancer risk index scores. Index scores were calculated for each person, and mean scores were created for each small area using Bayesian spatial modelling. Index scores were compared across area-level socio-economic quintiles and geographical remoteness subgroups. RESULTS:Although limited data was available in remote areas, observed patterns indicated that locations outside of major cities tended to have moderate-to-high index scores (mean 0.44, SD 0.25), while major cities showed higher proportions of areas with lower but more variable scores overall (mean 0.34 SD 0.21). Risk scores were higher in disadvantaged areas (0.39, SD 0.21) than advantaged areas (0.33. SD 0.21). This trend was consistent across education, occupation, and economic resources indices. CONCLUSION:People living further away from major cities and in socioeconomically disadvantaged areas may be more likely to have multiple modifiable health behaviours for colorectal cancer. When applied to a geographically representative dataset, the Index may be used to inform targeted health promotion interventions and localised community-based actions to reduce colorectal cancer risk and close the socio-economic and geographical gaps in incidence and mortality.

Spatiotemporal modelling is increasingly employed to understand how geographic patterns of disease vary over time; however, most studies report minimal information, often neglecting uncertainty altogether. This represents a missed opportunity to understand changes in disease risk at a fine granularity and to provide a valuable, actionable evidence base.This study describes generalisable methods for extracting key metrics from the outputs of Bayesian relative risk spatiotemporal models for epidemiological applications, such as the magnitude of relative change between defined periods and the level of evidence that any change was non-zero. Analytical approaches are introduced for identifying types of cancers or areas with particularly notable changes in rates and for visualising areas where both high rates and large changes in rates coincide. These metrics and analytical approaches enhance the interpretability of complex spatiotemporal patterns and can inform targeted interventions and support evidence-based prioritisation.To demonstrate these methods, we present a novel, flexible Bayesian hierarchical spatiotemporal disease model and describe how different implementations of the model can be used to understand either temporal changes by geographic area or shifts in geographic patterns over time. Variations on model specifications are described to support different temporal and space-time interaction models. Finally, we discuss practical considerations and challenges encountered in implementing spatiotemporal models.Overall, this work demonstrates how spatiotemporal modelling outputs can be systematically interpreted to generate actionable insights for public health planning and evaluation.

Introduction In Australia, around 85% of children survive childhood cancer. Yet, up to 80% of survivors experience subsequent adverse health conditions called late effects, largely attributed to cancer treatment. The LACE study is a population-based linked data resource that aims to facilitate the investigation of childhood cancer and its treatment and the impact on late effects for childhood cancer survivors. Methods The study links the Australian Childhood Cancer Registry to administrative cross-jurisdictional health and education data to enable ongoing follow-up of outcomes for childhood cancer survivors. The study population includes all Australian children aged less than 15 years, diagnosed with cancer 1983-2021, and comparison groups comprising siblings of childhood cancer patients and a random sample of children from the general population frequency matched by age, sex and residential location to cases. Results To date, the case cohort includes 25,226 children diagnosed with cancer, with longest follow-up to the age of 53 years. The most commonly diagnosed childhood cancers were leukaemia and related cancers (n=8182, 32.4%), followed by central nervous system and related cancers (n=5850, 23.2%), and lymphomas and reticuloendothelial neoplasms (n=2568, 10.2%). Overall, 16,314 (64.7%) children underwent chemotherapy, 5555 (22.0%) received radiotherapy and 7300 (28.9%) had surgical treatment for their cancer, with immunotherapy use reported for 641 (2.5%), hormonal therapy for 4549 (18.0%) and ancillary therapies for 2581 (10.2%). A total of 19,321 (76.6%) cases were alive at the end of the study. Conclusion This new comprehensive national data linkage resource represents a valuable asset that will facilitate research to identify the risk of late effects and effective follow-up care to inform counselling patients and their families, as well as guidelines, models of care and personalised follow-up care plans. Further, it will enable identification of inequities in healthcare access and outcomes across population sub-groups.

BACKGROUND:Cutaneous T-cell lymphomas (CTCLs) are rare with distinct diagnostic challenges. Equitable access to cancer care is a recognized priority, internationally. To date, the geospatial distribution of CTCL has not been definitively studied. Understanding the incidence and geographical distribution of patients with CTCL are critical first steps towards the ultimate goal of equity of care. Geospatial analyses also allow the opportunity to explore environmental causative factors: for CTCL, the contribution of solar ultraviolet (UV) radiation on causation remains unclear. OBJECTIVES:We investigate geospatial patterns of CTCL incidence across Australia, compare with all rare cancers, and consider solar UV exposure on causality and diagnosis rates. METHODS:All CTCL diagnoses (1 January 2000 to 31 December 2019) were obtained from the nationwide dataset. Areas of residence were collected according to nationally approved definitions. Bayesian spatial incidence models were applied. Geospatial distributions were visually analysed. RESULTS:The CTCL age-standardised incidence rate was 7.7 (95% confidence interval 7.4-7.9) per million people per year in Australia. Diagnostic disparity was seen between Australian states/territories, with lower diagnosis rates in rural/remote and socioeconomically disadvantaged areas. Incidence exceeded the national average within more densely populated capital cities. Visual comparisons of the geospatial distribution of CTCL revealed marked discordances with the geospatial patterns of all rare cancers and solar UV in Australia. CONCLUSIONS:Geographical heterogeneity in CTCL exists across Australia. Incidence reflects population density. Geospatial patterns of CTCL differ substantially from all rare cancers, with implications for the unique diagnostic challenges and unmet needs of this patient population. The distribution of CTCL across Australia does not support a causative link with UV exposure. Further global evaluation of geospatial patterns is warranted.

Introduction:Population-based cancer registries are critical for monitoring cancer burden, informing policy, and guiding cancer control strategies. However, many registries face challenges in effectively communicating their data, due to limited resources and technical capacity. There is a growing need for accessible tools that support the visualization and dissemination of cancer statistics in engaging and interpretable formats. Methods:CaRDO (Cancer Registry Data Online) was developed by the Viertel Cancer Research Centre at Cancer Council Queensland as a free, secure, and user-friendly R package for creating interactive dashboards using population-level cancer data. Built on the R Shiny framework, CaRDO enables users with minimal programming experience to generate dashboards entirely offline. The tool incorporates standard epidemiological methods, including age-standardized rates, lifetime risk estimation, and segmented regression for trend analysis, while maintaining strong privacy safeguards. Results:CaRDO allows users to display cancer incidence and mortality data by sex, cancer type, year, age group, and measure (counts or rates), with visualizations of underlying temporal trends. All data processing occurs locally, and dashboards can be customized with plots that can be downloaded for use in reports or presentations. Example datasets and detailed guidance are provided to support implementation. The current version includes core dashboard elements, with flexibility for future expansion. Conclusions:CaRDO provides a timely and practical solution for enhancing the visibility and utility of cancer registry data. By removing financial and technical barriers, it supports under-resourced registries in fulfilling their essential role in cancer control through transparent, datadriven public health communication.

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OBJECTIVE:To estimate the number of cancer-related deaths that could be attributed to spatial disparities in survival. DESIGN:Cohort study of cancer registry data. SETTING:Australia, 1 January 2010 to 31 December 2019. MAIN OUTCOME MEASURES:The numbers and percentages of cancer-related deaths attributable to spatial disparities in survival were estimated by calculating the numbers of cancer-related deaths that would have occurred if all areas in Australia met or exceeded a benchmark 5-year survival rate. This benchmark corresponded to the survival rate of the area with survival better than 80% of all areas, with "area" referring to residential location at diagnosis. RESULTS:Of all 289 075 cancer-related deaths in Australia in 2010-2019, 33 892 (11.7%) were attributable to spatial disparities in survival. Although numbers were greatest in major cities, as remoteness and area disadvantage increased, the percentages of cancer-related deaths attributable to spatial disparities in survival increased. Of all cancer-related deaths in remote areas and the most socio-economically disadvantaged areas, 1569 of 5208 (30.1%) and 13 469 of 66 775 (20.2%) deaths were attributable to survival disparities, respectively. The highest numbers and percentages of attributable cancer deaths in remote areas were for rare cancers (529/1809 [29.3%]), lung cancer (300/1298 [23.1%]) and head and neck cancers (162/370 [43.8%]). In the most disadvantaged areas, rare cancers (3070/20 512 [15.0%]) and lung cancer (2640/18 057 [14.6%]) had the highest numbers of attributable cancer deaths. CONCLUSIONS:These findings quantify the impact of spatial disparities in survival and highlight the need for equitable access to diagnostic and treatment services across Australia.

BACKGROUND:Monitoring cancer stage is vital to interpret cancer incidence and survival patterns, yet there are currently no cancer stage estimates by small areas across Australia, despite demonstrated large disparities in cancer incidence and survival. While cancer stage data is not routinely collected in Australia, a pilot project collected stage information nationwide in 2011. METHODS:Data on all primary invasive melanoma, female breast and prostate cancers (stages 1-4) diagnosed during 2011 in Australia were categorised into early and intermediate/advanced stage at diagnosis. Bayesian spatial models were used to estimate standardised incidence rates (SIRs) and proportions of cancer stage across 2148 statistical areas level 2. The correlation between early and more advanced cancer rates was explored using exceedance probabilities. RESULTS:Both melanoma and prostate cancer had mainly early stage diagnoses. There was large variation in rates across the nation, and also substantial correlation between SIRs of early and more advanced stage for melanoma and prostate cancer. In contrast, breast cancer had a higher proportion of advanced cancers diagnosed, less pronounced variation in rates and limited correlation between early and more advanced stage SIRs. The proportion of cases diagnosed as early stage varied across Australia by type of cancer. CONCLUSION:This study uncovered important spatial patterns in the diagnosis of cancer by stage across the country, which varied by cancer type and location. There is an urgent need to have contemporary information about stage at diagnosis routinely included in population-based cancer registries across the country.

OBJECTIVE:To identify the contribution of psychosocial characteristics, supportive care needs, or quality of life on breast cancer survival outcomes. METHODS:This study used data from a population-based longitudinal study involving women diagnosed with invasive breast cancer (n = 3326, response rate = 71%) in Queensland, Australia, 2010-2013, and followed up to 2020. Flexible parametric survival models were used to identify which factors were associated with survival outcomes. Model fit was assessed using D and R D 2 ${R}_{D}^{2}$ statistics. RESULTS:Unmet physical and daily living needs, social support, age, stage at diagnosis, tumour grade, clinical subtype and mode of detection explained 39% of survival variability ( R D 2 ${R}_{D}^{2}$ 0.39; 95% CI 0.33-0.44), with a Harrell's C statistic of 0.84 (95% CI 0.81-0.86). Unmet physical and daily living needs and social support, which fall under the categories of supportive care needs and psychosocial characteristics respectively, were identified as key factors that predict breast cancer survival, explaining 3% of survival variability. When compared to women who had less unmet physical needs and adequate social support (5-year survival: 96.6%, 95% CI 92%-99%), those who had more unmet physical needs and limited social support had poorer breast cancer-specific survival (5-year survival: 86.8%, 95% CI 72%-95%). CONCLUSION:The study found that unmet physical and daily living needs and social support play a marginal but significant role in influencing breast cancer outcomes. The findings enhance the current literature regarding the impact of psychosocial characteristics and supportive care needs on breast cancer survival and suggest that integrating psychosocial support and interventions alongside medical treatment may further improve the survival outcomes for women diagnosed with breast cancer.

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Background Women have better survival than men patients with colorectal cancer (CRC), but the extent to which this is due to multimorbidity is unclear. Methods A population-based study of 1843 patients diagnosed with CRC in Australia. Data included patient's demographics, multimorbidity, tumour histology, cancer stage, and treatment. We estimated the risks of all-cause mortality and cause-specific mortality due to cancer or non-cancer causes. Results Men had lower survival than women (P <= 0.010) amongst those diagnosed at Stages I-III (15-year survival: 56.0% vs 68.0%, 48.5% vs 60.7%, 34.8% vs 47.5%, respectively), excepting Stage IV (14.4% vs 12.6%; P = 0.18). Married men exhibit better survival than those who were never married (P = 0.006). Heart attacks (9.9% vs 4.3%, P < 0.001) and emphysema (4.8% vs 2.1%, P = 0.004) were more prevalent in men than women. Comorbid stroke and high cholesterol (adjusted hazard ratio, AHR = 2.22, 95% confidence interval, CI = 1.17-4.21, P = 0.014) and leukaemia (AHR = 6.36, 95% CI = 3.08-13.1, P < 0.001) increased the risk of cancer death for men only. For women, diabetes increased the risk of all-cause death (AHR = 1.38, 95% CI = 1.02-1.86, P = 0.039) and high blood pressure increased the risk of death due to non-cancer causes (AHR = 2.00, 95% CI = 1.36-2.94, P < 0.001). Conclusion Separate models of CRC care are needed for men and women with consideration of multimorbidity and social factors.

Supplementary Figure S2 shows article’s study period in years

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