Spatial patterns of prostate-specific antigen testing in asymptomatic men across Australia: a population-based cohort study, 2017-2018.

OBJECTIVES: In Australia, while prostate-specific antigen (PSA) testing rates vary by broad area-based categories of remoteness and socio-economic status, little is known about the extent of variation within them. This study aims to describe the small-area variation in PSA testing across Australia. STUDY DESIGN: This was a retrospective population-based cohort study. METHODS: We received data for PSA testing from the Australian Medicare Benefits Schedule. The cohort included men (n = 925,079) aged 50-79 years who had at least one PSA test during 2017-2018. A probability-based concordance was applied across multiple iterations (n = 50) to map each postcode to small areas (Statistical Areas 2; n = 2,129). For each iteration, a Bayesian spatial Leroux model was used to generate smoothed indirectly standardized incidence ratios across each small area, with estimates combined using model averaging. RESULTS: About a quarter (26%) of the male population aged 50-79 years had a PSA test during 2017-2018. Testing rates among small areas varied 20-fold. Rates were higher (exceedance probability>0.8) compared with the Australian average in the majority of small areas in southern Victoria and South Australia, south-west Queensland, and some coastal regions of Western Australia but lower (exceedance probability<0.2) in Tasmania and Northern Territory. CONCLUSIONS: The substantial geographical variation in PSA testing rates across small areas of Australia may be influenced by differences in access to and guidance provided by clinicians and attitudes and preferences of men. Greater understanding of PSA testing patterns by subregions and how these patterns relate to health outcomes could inform evidence-based approaches to identifying and managing prostate cancer risk.

Cancer survival differentials for Aboriginal and Torres Strait Islander peoples in Queensland: the impact of remoteness.

PURPOSE: In Australia, Aboriginal and Torres Strait Islander peoples (First Nations population) often have low overall cancer survival, as do all residents of geographically remote areas. This study aimed to quantify the survival disparity between First Nations and other Queenslanders for 12 common cancer types by remoteness areas. METHODS: For all Queensland residents aged 20-89 years diagnosed with a primary invasive cancer during 1997-2016, we ran flexible parametric survival models incorporating age, First Nations status, sex, diagnosis time period, area-level socioeconomic status, remoteness categories and where appropriate, broad cancer type. Three survival measures were predicted: cause-specific survival, survival differences and the comparative survival ratio, each standardised to First Nations peoples' covariate distributions. RESULTS: The standardised five-year cause-specific cancer survival was 60% for urban First Nations and 65% for other Queenslanders, while remote residents were 54% (First Nations) and 58% (other). The absolute survival differential between First Nations and other Queenslanders was often similar, regardless of remoteness of residence. The greatest absolute difference in five-year standardised cancer survival was for head and neck cancers, followed by cervical cancer. The five-year comparative survival ratio (First Nations: other Queenslanders) for urban cancer patients was 0.91 (95% CI 0.90-0.93), similar to outer regional, inner regional and remote areas. The greatest comparative survival differential was for oesophageal cancer. CONCLUSION: First Nations' survival inequalities are largely independent of geographical remoteness. It remains a priority to determine the contribution of other potential factors such as the availability of culturally acceptable diagnostic, management and/or support services.

Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review.

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.