Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.

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.

Authentication of smart grid communications using quantum key distribution.

Smart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid's reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks.

Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data.

Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors.

Correction to 'Augmenting disease maps: a Bayesian meta-analysis approach'.

[This corrects the article DOI: 10.1098/rsos.192151.

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics.

Background: Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancer types over 2148 small areas across Australia.

Methods: The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data.

Evaluating the impact of a small number of areas on spatial estimation.

Background: There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study.

Augmenting disease maps: a Bayesian meta-analysis approach.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps.

Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing.

Background: Many methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or global mean. Smoothing is important for several reasons, not least of all because it increases predictive robustness and reduces uncertainty of the estimates.

Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling.

A range of Bayesian models have been used to describe spatial and temporal patterns of disease in areal unit data. In this study, we applied two Bayesian spatio-temporal conditional autoregressive (ST CAR) models, one of which allows discontinuities in risk between neighbouring areas (creating 'groups'), to examine dengue fever patterns. Data on annual (2002-2017) and monthly (January 2013 - December 2017) dengue cases and climatic factors over 14 geographic areas were obtained for Makassar, Indonesia.

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia.

Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue.

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates.

Background: It is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia.

Methods: The Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area.

Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference.

Background: When analysing spatial data, it is important to account for spatial autocorrelation. In Bayesian statistics, spatial autocorrelation is commonly modelled by the intrinsic conditional autoregressive prior distribution. At the heart of this model is a spatial weights matrix which controls the behaviour and degree of spatial smoothing.

Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation.

Objectives: To compare two Bayesian models capable of identifying unusual and unstable temporal patterns in spatiotemporal data.

Setting: Annual counts of mammography screening users from each statistical local area (SLA) in Brisbane, Australia, recorded between 1997 and 2008 inclusive.

Primary Outcome Measures: Mammography screening counts.

Prospective study of diagnostic accuracy comparing prostate cancer detection by transrectal ultrasound-guided biopsy versus magnetic resonance (MR) imaging with subsequent MR-guided biopsy in men without previous prostate biopsies.

Background: The current diagnosis of prostate cancer (PCa) uses transrectal ultrasound-guided biopsy (TRUSGB). TRUSGB leads to sampling errors causing delayed diagnosis, overdetection of indolent PCa, and misclassification. Advances in multiparametric magnetic resonance imaging (mpMRI) suggest that imaging and selective magnetic resonance (MR)-guided biopsy (MRGB) may be superior to TRUSGB.