The Impact of Rurality and Disadvantage on the Diagnostic Interval for Breast Cancer in a Large Population-Based Study of 3202 Women in Queensland, Australia.

Delays in diagnosing breast cancer (BC) can lead to poorer outcomes. We investigated factors related to the diagnostic interval in a population-based cohort of 3202 women diagnosed with BC in Queensland, Australia. Interviews ascertained method of detection and dates of medical/procedural appointments, and clinical information was obtained from medical records. Time intervals were calculated from self-recognition of symptoms (symptom-detected) or mammogram (screen-detected) to diagnosis (diagnostic interval (DI)). The cohort included 1560 women with symptom-detected and 1642 with screen-detected BC. Symptom-detected women had higher odds of DI of >60 days if they were Indigenous (OR = 3.12, 95% CI = 1.40, 6.98); lived in outer regional (OR = 1.50, 95% CI = 1.09, 2.06) or remote locations (OR = 2.46, 95% CI = 1.39, 4.38); or presented with a "non-lump" symptom (OR = 1.84, 95% CI = 1.43, 2.36). For screen-detected BC, women who were Indigenous (OR = 2.36, 95% CI = 1.03, 5.80); lived in remote locations (OR = 2.35, 95% CI = 1.24, 4.44); or disadvantaged areas (OR = 1.69, 95% CI = 1.17, 2.43) and attended a public screening facility (OR = 2.10, 95% CI = 1.40, 3.17) had higher odds of DI > 30 days. Our study indicates a disadvantage in terms of DI for rural, disadvantaged and Indigenous women. Difficulties in accessing primary care and diagnostic services are evident. There is a need to identify and implement an efficient and effective model of care to minimize avoidable longer diagnostic intervals

A prognostic survival model for women diagnosed with invasive breast cancer in Queensland, Australia.

PURPOSE: Prognostic models can help inform patients on the future course of their cancer and assist the decision making of clinicians and patients in respect to management and treatment of the cancer. In contrast to previous studies considering survival following treatment, this study aimed to develop a prognostic model to quantify breast cancer-specific survival at the time of diagnosis. METHODS: A large (n = 3323), population-based prospective cohort of women were diagnosed with invasive breast cancer in Queensland, Australia between 2010 and 2013, and followed up to December 2018. Data were collected through a validated semi-structured telephone interview and a self-administered questionnaire, along with data linkage to the Queensland Cancer Register and additional extraction from medical records. Flexible parametric survival models, with multiple imputation to deal with missing data, were used. RESULTS: Key factors identified as being predictive of poorer survival included more advanced stage at diagnosis, higher tumour grade, "triple negative" breast cancers, and being symptom-detected rather than screen detected. The Harrell's C-statistic for the final predictive model was 0.84 (95% CI 0.82, 0.87), while the area under the ROC curve for 5-year mortality was 0.87. The final model explained about 36% of the variation in survival, with stage at diagnosis alone explaining 26% of the variation. CONCLUSIONS: In addition to confirming the prognostic importance of stage, grade and clinical subtype, these results highlighted the independent survival benefit of breast cancers diagnosed through screening, although lead and length time bias should be considered. Understanding what additional factors contribute to the substantial unexplained variation in survival outcomes remains an important objective

Geographic disparities in previously diagnosed health conditions in colorectal cancer patients are largely explained by age and area level disadvantage

© 2018 Goodwin, March, Ireland, Crawford-Williams, Ng, Baade, Chambers, Aitken and Dunn. Background: Geographical disparity in colorectal cancer (CRC) survival rates may be partly due to aging populations and disadvantage in more remote locations; factors that also impact the incidence and outcomes of other chronic health conditions. The current study investigates whether geographic disparity exists amongst previously diagnosed health conditions in CRC patients above and beyond age and area-level disadvantage and whether this disparity is linked to geographic disparity in CRC survival. Methods: Data regarding previously diagnosed health conditions were collected via computer-assisted telephone interviews with a cross-sectional sample of n = 1,966 Australian CRC patients between 2003 and 2004. Ten-year survival outcomes were acquired in December 2014 from cancer registry data. Multivariate logistic regressions were applied to test associations between previously diagnosed health conditions and survival rates in rural, regional, and metropolitan areas. Results: Results suggest that only few geographical disparities exist in previously diagnosed health conditions for CRC patients and these were largely explained by socio-economic status and age. Living in an inner regional area was associated with cardio-vascular conditions, one or more respiratory diseases, and multiple respiratory diagnoses. Higher occurrences of these conditions did not explain lower CRC-specific 10 years survival rates in inner regional Australia. Conclusion: It is unlikely that health disparities in terms of previously diagnosed conditions account for poorer CRC survival in regional and remote areas. Interventions to improve the health of regional CRC patients may need to target issues unique to socio-economic disadvantage and older age

Diagnostic and treatment pathways for men with prostate cancer in Queensland: investigating spatial and demographic inequalities

Background: Patterns of diagnosis and management for men diagnosed with prostate cancer in Queensland, Australia, have not yet been systematically documented and so assumptions of equity are untested. This longitudinal study investigates the association between prostate cancer diagnostic and treatment outcomes and key area-level characteristics and individual-level demographic, clinical and psychosocial factors.---------- Methods/Design: A total of 1064 men diagnosed with prostate cancer between February 2005 and July 2007 were recruited through hospital-based urology outpatient clinics and private practices in the centres of Brisbane, Townsville and Mackay (82% of those referred). Additional clinical and diagnostic information for all 6609 men diagnosed with prostate cancer in Queensland during the study period was obtained via the population-based Queensland Cancer Registry. Respondent data are collected using telephone and self-administered questionnaires at pre-treatment and at 2 months, 6 months, 12 months, 24 months, 36 months, 48 months and 60 months post-treatment. Assessments include demographics, medical history, patterns of care, disease and treatment characteristics together with outcomes associated with prostate cancer, as well as information about quality of life and psychological adjustment. Complementary detailed treatment information is abstracted from participants’ medical records held in hospitals and private treatment facilities and collated with health service utilisation data obtained from Medicare Australia. Information about the characteristics of geographical areas is being obtained from data custodians such as the Australian Bureau of Statistics. Geo-coding and spatial technology will be used to calculate road travel distances from patients’ residences to treatment centres. Analyses will be conducted using standard statistical methods along with multilevel regression models including individual and area-level components.---------- Conclusions: Information about the diagnostic and treatment patterns of men diagnosed with prostate cancer is crucial for rational planning and development of health delivery and supportive care services to ensure equitable access to health services, regardless of geographical location and individual characteristics. This study is a secondary outcome of the randomised controlled trial registered with the Australian New Zealand Clinical Trials Registry (ACTRN12607000233426

Stage at diagnosis for childhood solid cancers in Australia: A population-based study

BACKGROUND: Stage of cancer at diagnosis is one of the strongest predictors of survival and is essential for population cancer surveillance, comparison of cancer outcomes and to guide national cancer control strategies. Our aim was to describe, for the first time, the distribution of cases by stage at diagnosis and differences in stage-specific survival on a population basis for a range of childhood solid cancers in Australia. METHODS: The study cohort was drawn from the population-based Australian Childhood Cancer Registry and comprised children (<15 years) diagnosed with one of 12 solid malignancies between 2006 and 2014. Stage at diagnosis was assigned according to the Toronto Paediatric Cancer Stage Guidelines. Observed (all cause) survival was calculated using the Kaplan-Meier method, with follow-up on mortality available to 31 December 2015. RESULTS: Almost three-quarters (1256 of 1760 cases, 71%) of children in the study had localised or regional disease at diagnosis, varying from 43% for neuroblastoma to 99% for retinoblastoma. Differences in 5-year observed survival by stage were greatest for osteosarcoma (localised 85% (95% CI = 72%-93%) versus metastatic 37% (15%-59%)), neuroblastoma (localised 98% (91%-99%) versus metastatic 60% (52%-67%)), rhabdomyosarcoma (localised 85% (71%-93%) versus metastatic 53% (34%-69%)), and medulloblastoma (localised 69% (61%-75%) versus metastases to spine 42% (27%-57%)). CONCLUSION: The stage-specific information presented here provides a basis for comparison with other international population cancer registries. Understanding variations in survival by stage at diagnosis will help with the targeted formation of initiatives to improve outcomes for children with cancer

Estadificación del cáncer infantil para registros de base poblacional

La recogida de información internacional fiable sobre estadificación de cáncer infantil por los registros de cáncer de base poblacional es esencial para el análisis epidemiológico y la realización de comparaciones evaluativas internacionales explicativas de la incidencia y los resultados asistenciales. En 2014 la Unión International Contra el Cáncer (UICC), el Dana-Farber Cancer Institute y el Hospital for Sick Children de Toronto, convocaron una reunión de consenso para abordar la ausencia de información consistente en la estadificación del cáncer infantil en los registros de cáncer poblacionales. Para cada subconjunto de los grupos/subgrupos diagnósticos mayores de cáncer infantil, en la reunión fueron revisados todos los sistemas de estadificación específicos de cada enfermedad utilizados habitualmente y se recomendó el más adecuado para utilizar en los registros de cáncer de base poblacional. Los sistemas de estadificación recomendados están enumerados como Guías de Toronto para la Estadificación del Cáncer Pediátrico. Las Guías recomiendan sistemas de estadificación específicos para la Leucemia Linfoblástica Aguda, Leucemia Mieloblástica Aguda, Linfoma de Hodgkin, Linfoma no-Hodgkin, Neuroblastoma, Tumor de Wilms, Rabdomiosarcoma, Sarcomas de Tejidos Blandos no-Rabdomiosarcoma, Osteosarcoma, Sarcoma de Ewing, Retinoblastoma, Hepatoblastoma, Tumor de Células Germinales (Cáncer Testicular y Ovárico), Meduloblastoma y Ependimoma. En este texto se proporcionan descripciones detalladas de los sistemas de estadificación recomendados en las Guías, para ayudar a los registros poblacionales de cáncer a recoger información internacionalmente consistente y comparable sobre el estadio del cáncer infantil al diagnóstico utilizando los documentos clínicos disponibles. La viabilidad y validez de estas Guías se ha evaluado con éxito en la práctica2. Están avaladas por el proyecto Factores pronósticos TNM de la UICC y publicadas en la Clasificación TNM de los Tumores Malignos UICC 8ª edición

A Cost-Utility Analysis of Prostate Cancer Screening in Australia

Background and Objectives: The Göteborg randomised population-based prostate cancer screening trial demonstrated that Prostate Specific Antigen (PSA) based screening reduces prostate cancer deaths compared with an age matched control group. Utilising the prostate cancer detection rates from this study we have investigated the clinical and cost-effectiveness of a similar PSA-based screening strategy for an Australian population of men aged 50-69 years. Methods: A decision model that incorporated Markov processes was developed from a health system perspective.The base case scenario compared a population-based screening programme with current opportunistic screening practices. Costs, utility values, treatment patterns and background mortality rates were derived from Australian data. All costs were adjusted to reflect July 2015 Australian dollars. An alternative scenario compared systematic with opportunistic screening but with optimisation of active surveillance (AS) uptake in both groups. A discount rate of 5% for costs and benefits was utilised. Univariate and probabilistic sensitivity analyses were performed to assess the effect of variable uncertainty on model outcomes. Results: Our model very closely replicated the number of deaths from both prostate cancer and background mortality in the Göteborg study. The incremental cost per quality-adjusted life-year (QALY) for PSA screening was $AU147,528. However, for years of life gained (LYGs) PSA based screening ($AU45,890/LYG) appeared more favourable. Our alternative scenario with optimised AS improved cost-utility to $AU45,881/QALY, with screening becoming cost-effective at a 92$AU50,000/QALY. It appears more cost-effective if LYGs are used as the relevant outcome, and is more cost effective than the established Australian breast cancer screening programme on this basis. Optimised utilisation of AS increases the cost-effectiveness of prostate cancer screening dramatically

Cholesterol and the risk of grade-specific prostate cancer incidence: evidence from two large prospective cohort studies with up to 37 years' follow up

&lt;b&gt;Background&lt;/b&gt; High cholesterol may be a modifiable risk factor for prostate cancer but results have been inconsistent and subject to potential "reverse causality" where undetected disease modifies cholesterol prior to diagnosis.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods&lt;/b&gt; We conducted a prospective cohort study of 12,926 men who were enrolled in the Midspan studies between 1970 and 1976 and followed up to 31st December 2007. We used Cox-Proportional Hazards Models to evaluate the association between baseline plasma cholesterol and Gleason grade-specific prostate cancer incidence. We excluded cancers detected within at least 5 years of cholesterol assay.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; 650 men developed prostate cancer in up to 37 years' follow-up. Baseline plasma cholesterol was positively associated with hazard of high grade (Gleason score[greater than or equal to]8) prostate cancer incidence (n=119). The association was greatest among men in the 4th highest quintile for cholesterol, 6.1 to &#60;6.69 mmol/l, Hazard Ratio 2.28, 95% CI 1.27 to 4.10, compared with the baseline of &#60;5.05 mmol/l. This association remained significant after adjustment for body mass index, smoking and socioeconomic status.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions&lt;/b&gt; Men with higher cholesterol are at greater risk of developing high-grade prostate cancer but not overall risk of prostate cancer. Interventions to minimise metabolic risk factors may have a role in reducing incidence of aggressive prostate cancer

A multilevel study of the determinants of area-level inequalities in colorectal cancer survival

Background: In Australia, associations between geographic remoteness, socioeconomic disadvantage, and colorectal cancer (CRC) survival show that survival rates are lowest among residents of geographically remote regions and those living in disadvantaged areas. At present we know very little about the reasons for these inequalities, hence our capacity to intervene to reduce the inequalities is limited. Methods/Design: This study, the first of its type in Australia, examines the association between CRC survival and key area- and individual-level factors. Specifically, we will use a multilevel framework to investigate the possible determinants of area- and individual-level inequalities in CRC survival and quantify the relative contribution of geographic remoteness, socioeconomic and demographic factors, disease stage, and access to diagnostic and treatment services, to these inequalities. The multilevel analysis will be based on survival data relating to people diagnosed with CRC in Queensland between 1996 and 2005 (n = 22,723) from the Queensland Cancer Registry (QCR), area-level data from other data custodians such as the Australian Bureau of Statistics, and individual-level data from the QCR (including extracting stage from pathology records) and Queensland Hospitals. For a subset of this period (2003 and 2004) we will utilise more detailed, individual-level data (n = 1,966) covering a greater range of risk factors from a concurrent research study. Geo-coding and spatial technology will be used to calculate road travel distances from patients’ residence to treatment centres. The analyses will be conducted using a multilevel Cox proportional hazards model with Level 1 comprising individual-level factors (e.g. occupation) and level 2 area level indicators of remoteness and area socioeconomic disadvantage. Discussion: This study focuses on the health inequalities for rural and disadvantaged populations that have often been documented but poorly understood, hence limiting our capacity to intervene. This study utilises and develops emerging statistical and spatial technologies that can then be applied to other cancers and health outcomes. The findings of this study will have direct implications for the targeting and resourcing of cancer control programs designed to reduce the burden of colorectal cancer, and for the provision of diagnostic and treatment services