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Indigenous women continue to experience a disproportionately higher burden of cervical cancer than non‐Indigenous women in Australia. The National Indigenous Cervical Screening Project used probabilistic record linkage to combine population‐based administrative databases and identify Indigenous women on Pap Smear Registers. This study aimed to quantify the spatial variation by local government areas (LGAs) for Indigenous and non‐Indigenous women in Queensland in cervical screening participation rates and related outcomes. Empirical Bayes local geostatistical smoothing was performed to reduce the likelihood of spurious variation between small areas. The cohort included 1,091,260 women (2 per cent Indigenous) aged 20 to 69 with 2,393,708 Pap smears between 2006 and 2011. Indigenous women had smoothed LGA‐specific 5‐year participation rates (interquartile range (IQR) 38.9–53.3 per 100 eligible women) consistently lower than non‐Indigenous women (IQR 80.7–85.3). The non‐overlapping confidence intervals of these rates suggest that the Indigenous differential was significant. Compared with Indigenous women, non‐Indigenous women had consistently lower and more stable prevalence rates of histologically confirmed high grade abnormalities (IQR 8.0–10.1 versus 15.0–21.3 per 1,000 screened women). Although the LGA‐specific rates also suggest that a higher proportion of non‐Indigenous women were followed‐up within two months of an abnormal screening result, the wide confidence intervals for these estimates limit our ability to draw definitive conclusions about spatial patterns for this outcome. These findings highlight the importance of continued monitoring and ongoing efforts to identify drivers of these patterns and develop effective strategies to improve participation and potentially reduce the cervical cancer burden among Indigenous women. [ABSTRACT FROM AUTHOR]

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Although early diagnosis and improved treatment can reduce breast cancer mortality, there still appears to be a geographic differential in patient outcomes. This study aims to determine and quantify spatial inequalities in intended adjuvant (radio-, chemo- and hormonal) therapy usage among women with screen-detected breast cancer in Queensland, Australia.Methods: Linked population-based datasets from BreastScreen Queensland and the Queensland Cancer Registry during 1997-2008 for women aged 40-89 years were used. We adopted a Bayesian shared spatial component model to evaluate the relative intended use of each adjuvant therapy across 478 areas as well as common spatial patterns between treatments.Results: Women living closer to a cancer treatment facility were more likely to intend to use adjuvant therapy. This was particularly marked for radiotherapy when travel time to the closest radiation facility was 4 + h (OR =0.41, 95 % CrI: [0.23, 0.74]) compared to <1 h. The shared spatial effect increased towards the centres with concentrations of radiotherapy facilities, in north-east (Townsville) and south-east (Brisbane) regions of Queensland. Moreover, the presence of residual shared spatial effects indicates that there are other unmeasured geographical barriers influencing women's treatment choices.Conclusions: This highlights the need to identify the additional barriers that impact on treatment intentions among women diagnosed with screen-detected breast cancer, particularly for those women living further away from cancer treatment centers. [ABSTRACT FROM AUTHOR]

This study investigated the impact of spatial location on the effectiveness of population-based breast screening in reducing breast cancer mortality compared to other detection methods among Queensland women. The analysis was based on linked population-based datasets from BreastScreen Queensland and the Queensland Cancer Registry for the period of 1997-2008 for women aged less than 90 years at diagnosis. A Bayesian hierarchical regression modelling approach was adopted and posterior estimation was performed using Markov Chain Monte Carlo techniques. This approach accommodated sparse data resulting from rare outcomes in small geographic areas, while allowing for spatial correlation and demographic influences to be included. A relative survival model was chosen to evaluate the relative excess risk for each breast cancer related factor. Several models were fitted to examine the influence of demographic information, cancer stage, geographic information and detection method on women's relative survival. Overall, the study demonstrated that including the detection method and geographic information when assessing the relative survival of breast cancer patients helped capture unexplained and spatial variability. The study also found evidence of better survival among women with breast cancer diagnosed in a screening program than those detected otherwise, as well as lower risk for those residing in a more urban or socio-economically advantaged region, even after adjusting for tumour stage, environmental factors and demographics. However, no evidence of dependency between method of detection and geographic location was found. This project provides a sophisticated approach to examining the benefit of a screening program while considering the influence of geographic factors. [ABSTRACT FROM AUTHOR]

The article discusses cancer survival among Indigenous and non-Indigenous people in Queensland from 1997 to 2006. It is revealed that Aboriginal patients have lower survival rates compare to non-Indigenous patients early after cancer diagnosis. Two years after diagnosis, it is observed that Indigenous cancer survivors have similar outcomes to non-Indigenous patients. It is suggested to continue studying the poor survival rates of Indigenous patients early after cancer diagnosis.

Background: Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival., Methods: Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20-84 years diagnosed during 1997-2007 from Queensland, Australia., Results: Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients., Conclusions: With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings.

This study examines the influence of cancer stage, distance to treatment facilities and area disadvantage on breast and colorectal cancer spatial survival inequalities. We also estimate the number of premature deaths after adjusting for cancer stage to quantify the impact of spatial survival inequalities. Population-based descriptive study of residents aged <90 years in Queensland, Australia diagnosed with primary invasive breast (25,202 females) or colorectal (14,690 males, 11,700 females) cancers during 1996-2007. Bayesian hierarchical models explored relative survival inequalities across 478 regions. Cancer stage and disadvantage explained the spatial inequalities in breast cancer survival, however spatial inequalities in colorectal cancer survival persisted after adjustment. Of the 6,019 colorectal cancer deaths within 5 years of diagnosis, 470 (8%) were associated with spatial inequalities in non-diagnostic factors, i.e. factors beyond cancer stage at diagnosis. For breast cancers, of 2,412 deaths, 170 (7%) were related to spatial inequalities in non-diagnostic factors. Quantifying premature deaths can increase incentive for action to reduce these spatial inequalities., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: Strategies for cancer reduction and management are targeted at both individual and area levels. Area-level strategies require careful understanding of geographic differences in cancer incidence, in particular the association with factors such as socioeconomic status, ethnicity and accessibility. This study aimed to identify the complex interplay of area-level factors associated with high area-specific incidence of Australian priority cancers using a classification and regression tree (CART) approach., Methods: Area-specific smoothed standardised incidence ratios were estimated for priority-area cancers across 478 statistical local areas in Queensland, Australia (1998-2007, n = 186,075). For those cancers with significant spatial variation, CART models were used to identify whether area-level accessibility, socioeconomic status and ethnicity were associated with high area-specific incidence., Results: The accessibility of a person's residence had the most consistent association with the risk of cancer diagnosis across the specific cancers. Many cancers were likely to have high incidence in more urban areas, although male lung cancer and cervical cancer tended to have high incidence in more remote areas. The impact of socioeconomic status and ethnicity on these associations differed by type of cancer., Conclusions: These results highlight the complex interactions between accessibility, socioeconomic status and ethnicity in determining cancer incidence risk.

Background: Achieving health equity has been identified as a major challenge, both internationally and within Australia. Inequalities in cancer outcomes are well documented, and must be quantified before they can be addressed. One method of portraying geographical variation in data uses maps. Recently we have produced thematic maps showing the geographical variation in cancer incidence and survival across Queensland, Australia. This article documents the decisions and rationale used in producing these maps, with the aim to assist others in producing chronic disease atlases., Methods: Bayesian hierarchical models were used to produce the estimates. Justification for the cancers chosen, geographical areas used, modelling method, outcome measures mapped, production of the adjacency matrix, assessment of convergence, sensitivity analyses performed and determination of significant geographical variation is provided., Conclusions: Although careful consideration of many issues is required, chronic disease atlases are a useful tool for assessing and quantifying geographical inequalities. In addition they help focus research efforts to investigate why the observed inequalities exist, which in turn inform advocacy, policy, support and education programs designed to reduce these inequalities.

We examined the prospective association of childhood BMI z-score and BMI categories (normal or overweight) with young adult diabetes, controlling for early life, childhood, and adolescence factors. A subsample of 2,639 young adults from the Mater-University study of pregnancy (MUSP) and its outcomes, a prospective birth cohort who were born in Brisbane, Australia and for whom we had measured height and weight at 5 years and self-reported diabetes at age 21 years. The risk of developing diabetes by age 21 years was greater among young adults who had greater BMI z-score or were overweight at age 5 years than those who had normal BMI at age 5 years. Young adults who were overweight at age 5 years had an increased odds ratio of 2.60 (95% confidence interval (CI): 1.29, 5.22, in age- and sex-adjusted model) of experiencing diabetes by age 21 years. Adjustment for potential confounders and mediators including intrauterine environmental factors, childhood dietary patterns, television watching, participation in sports and exercise, and current weight, did not substantively alter these associations. Overweight and increasing BMI z-score at childhood is an independent predictor of young adult's type 1 and type 2 diabetes. Findings of this study suggest that childhood BMI may be central to the development and rising incidence of all diabetes.