Global trends in the incidence of hospital admissions for diabetes-related foot disease and amputations: a review of national rates in the 21st century.

AIMS/HYPOTHESIS: Diabetic foot disease (DFD) is a leading cause of hospital admissions and amputations. Global trends in diabetes-related amputations have been previously reviewed, but trends in hospital admissions for multiple other DFD conditions have not. This review analysed the published incidence of hospital admissions for DFD conditions (ulceration, infection, peripheral artery disease [PAD], neuropathy) and diabetes-related amputations (minor and major) in nationally representative populations. METHODS: PubMed and Embase were searched for peer-reviewed publications between 1 January 2001 and 5 May 2022 using the terms 'diabetes', 'DFD', 'amputation', 'incidence' and 'nation'. Search results were screened and publications reporting the incidence of hospital admissions for a DFD condition or a diabetes-related amputation among a population representative of a country were included. Key data were extracted from included publications and initial rates, end rates and relative trends over time summarised using medians (ranges). RESULTS: Of 2527 publications identified, 71 met the eligibility criteria, reporting admission rates for 27 countries (93% high-income countries). Of the included publications, 14 reported on DFD and 66 reported on amputation (nine reported both). The median (range) incidence of admissions per 1000 person-years with diabetes was 16.3 (8.4-36.6) for DFD conditions (5.1 [1.3-7.6] for ulceration; 5.6 [3.8-9.0] for infection; 2.5 [0.9-3.1] for PAD) and 3.1 (1.4-10.3) for amputations (1.2 [0.2-4.2] for major; 1.6 [0.3-4.3] for minor). The proportions of the reported populations with decreasing, stable and increasing admission trends were 80%, 20% and 0% for DFD conditions (50%, 0% and 50% for ulceration; 50%, 17% and 33% for infection; 67%, 0% and 33% for PAD) and 80%, 7% and 13% for amputations (80%, 17% and 3% for major; 52%, 15% and 33% for minor), respectively. CONCLUSIONS/INTERPRETATION: These findings suggest that hospital admission rates for all DFD conditions are considerably higher than those for amputations alone and, thus, the more common practice of reporting admission rates only for amputations may substantially underestimate the burden of DFD. While major amputation rates appear to be largely decreasing, this is not the case for hospital admissions for DFD conditions or minor amputation in many populations. However, true global conclusions are limited because of a lack of consistent definitions used to identify admission rates for DFD conditions and amputations, alongside a lack of data from low- and middle-income countries. We recommend that these areas are addressed in future studies. REGISTRATION: This review was registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/4TZFJ ).

Geographical and spatial disparities in the incidence and survival of rare cancers in Australia.

Rare cancers collectively account for around a quarter of cancer diagnoses and deaths. However, epidemiological studies are sparse. We describe spatial and geographical patterns in incidence and survival of rare cancers across Australia using a population-based cancer registry cohort of rare cancer cases diagnosed among Australians aged at least 15 years, 2007 to 2016. Rare cancers were defined using site- and histology-based categories from the European RARECARE study, as individual cancer types having crude annual incidence rates of less than 6/100 000. Incidence and survival patterns were modelled with generalised linear and Bayesian spatial Leroux models. Spatial heterogeneity was tested using the maximised excess events test. Rare cancers (n = 268 070) collectively comprised 22% of all invasive cancer diagnoses and accounted for 27% of all cancer-related deaths in Australia, 2007 to 2016 with an overall 5-year relative survival of around 53%. Males and those living in more remote or more disadvantaged areas had higher incidence but lower survival. There was substantial evidence for spatial variation in both incidence and survival for rare cancers between small geographical areas across Australia, with similar patterns so that those areas with higher incidence tended to have lower survival. Rare cancers are a substantial health burden in Australia. Our study has highlighted the need to better understand the higher burden of these cancers in rural and disadvantaged regions where the logistical challenges in their diagnosis, treatment and support are magnified.

Cost-effectiveness of guideline-based care provision for patients with diabetes-related foot ulcers: A modelled analysis using discrete event simulation.

AIMS: The provision of guideline-based care for patients with diabetes-related foot ulcers (DFU) in clinical practice is suboptimal. We estimated the cost-effectiveness of higher rates of guideline-based care, compared with current practice. METHODS: The costs and quality-adjusted life-years (QALYs) associated with current practice (30% of patients receiving guideline-based care) were compared with seven hypothetical scenarios with increasing proportion of guideline-based care (40%, 50%, 60%, 70%, 80%, 90% and 100%). Comparisons were made using discrete event simulations reflecting the natural history of DFU over a 3-year time horizon from the Australian healthcare perspective. Incremental cost-effectiveness ratios were calculated for each scenario and compared to a willingness-to-pay of AUD 28,000 per QALY. Probabilistic sensitivity analyses were conducted to incorporate joint parameter uncertainty. RESULTS: All seven scenarios with higher rates of guideline-based care were likely cheaper and more effective than current practice. Increased proportions compared with current practice resulted in between AUD 0.28 and 1.84 million in cost savings and 11-56 additional QALYs per 1000 patients. Probabilistic sensitivity analyses indicated that the finding is robust to parameter uncertainty. CONCLUSIONS: Higher proportions of patients receiving guideline-based care are less costly and improve patient outcomes. Strategies to increase the proportion of patients receiving guideline-based care are warranted.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Mapping the prevalence of cancer risk factors at the small area level in Australia.

BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Inpatient Fall Prediction Models: A Scoping Review.

INTRODUCTION: The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction. METHODS: This scoping review used methods recommended by the Arksey and O'Malley framework and its recent advances. PubMed, CINAHL, IEEE Xplore, and EMBASE databases were systematically searched. Studies reporting the development of inpatient fall risk prediction approaches were included. There was no restriction on language or recency. Reference lists and manual searches were also completed. Reporting quality was assessed using adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement (TRIPOD), where appropriate. RESULTS: Database searches identified 1,396 studies, 63 were included for scoping assessment and 45 for reporting quality assessment. There was considerable overlap in data sources and methods used for model development. Fall prediction models typically relied on features from patient assessments, including indicators of physical function or impairment, or cognitive function or impairment. All but two studies used patient information at or soon after admission and predicted fall risk over the entire admission, without consideration of post-admission interventions, acuity changes or length of stay. Overall, reporting quality was poor, but improved in the past decade. CONCLUSION: There was substantial homogeneity in data sources and prediction model development methods. Use of artificial intelligence, including machine learning with high-dimensional data, remains underexplored in the context of hospital falls. Future research should consider approaches with the potential to utilize high-dimensional data from digital hospital systems, which may contribute to greater performance and clinical usefulness.

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. METHODS: Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered. RESULTS: The simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised [Formula: see text] models performed similarly and better than the independent and localised [Formula: see text] models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice. CONCLUSION: Detecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.

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. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually. RESULTS: The results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas ( https://atlas.cancer.org.au ) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making. CONCLUSIONS: The success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

A flexible parametric approach to examining spatial variation in relative survival.

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.

Geographic variation in the intended choice of adjuvant treatments for women diagnosed with screen-detected breast cancer in Queensland.

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.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.

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.

The shock index predicts in-flight blood transfusion in aeromedical trauma patients.

OBJECTIVE: To define the utility of the Triage Revised Trauma Score (TRTS), GCS/Age/arterial Pressure (GAP) score, and shock index (SI) in predicting the need for in-flight blood product administration in civilian trauma patients transported by an aeromedical platform. METHODS: A retrospective chart review of 3582 aeromedical trauma cases was conducted. An initial TRTS, GAP score and SI were calculated for each patient, and the administration of in-flight blood products was also recorded. Receiver operating characteristic (ROC) curves were used to quantify the predictive discrimination of the TRTS, GAP score and SI on the need for in-flight blood product administration. RESULTS: The SI showed a superior predictive value compared to the TRTS and GAP score. The SI showed an area under the curve on the ROC curve of 0.85 in both primary and inter-hospital transfer cases, indicating reasonable predictive value. CONCLUSION: The SI demonstrates favourable test characteristics for predicting the need for in-flight blood product administration. Prospective validation of these results is warranted.

Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model.

This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.

Financial Aid in Children, Adolescents and Young Adult's Cancer Care: A Scoping Review.

Background: The financial burden resulting from cancers on families is higher when it arises in young people compared with older adults. Previous research has provided insight into the financial toxicities associated with childhood cancer, but less is known about the efficacy of financial aid systems in reducing the financial burden on families. We conducted a scoping review to identify the determinants of success and failure of financial aid. Methods: Five databases were searched for articles published between January 1, 2000 and December 1, 2022. Dual processes were used to screen and select studies. Through thematic content analysis, we identified barriers and enablers of financial aid, categorised by country income level. Results: From 17 articles, which were evenly split between high-income countries and upper middle- to low-income countries, four major themes emerged: (1) accessibility of support, (2) delivery of support, (3) administration, and (4) psychosocial factors. Within these themes, the enablers identified were (1) support navigators, (2) establishing a direct contact between donors and beneficiaries, (3) implementation of digital solutions to improve outreach, and (4) using cultural and community values to encourage donor engagement. Conclusions: This scoping review identified the determinants of success and failure of financial aid in supporting families in the context of childhood, adolescent, and young adult (CAYA) cancers. By understanding the barriers and enablers identified in this review, organizations could develop pragmatic evidence-based care models and policies to ensure access to assistance is equitable and appropriate for families experiencing CAYA cancers.

Improving the spatial and temporal resolution of burden of disease measures with Bayesian models.

This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.

An exploratory study of the gut microbiota in major depression with anxious distress.

OBJECTIVES: To explore differences in the diversity and composition of the gut microbiome between major depressive disorder (MDD) with and without anxious distress. METHODS: The study comprised 117 participants (79 female, 36 male, 2 other, mean age 38.2 ± 13.4 years) with a current major depressive episode (MDE) with (n = 63) and without (n = 54) the anxious distress specifier. A clinical psychologist administered the structured clinical interview for the DSM-5-RV to confirm a diagnosis of depression. Participants provided stool samples which were immediately frozen and stored at -80 °C. These samples were analysed using the Illumina 16S Metagenomics sequencing protocol in which the sequencing primers target the V3 and V4 regions of the 16S rRNA gene. Participants also completed mental health questionnaires to assess severity of depression (BDI-II), generalized anxiety (GAD-7), and stress (PSS). RESULTS: There were no significant group differences in α-diversity (Shannon's diversity Index; Simpson Index), richness (ACE; Chao1), (Pielou's) evenness, or beta diversity (Bray-Curtis dissimilarity index and weighted UniFrac distance) of gut bacteria. Significant group differences in the relative abundance of gut microbiota however were observed at each taxonomical level, including across 15 genera and 18 species. LIMITATIONS: This was an exploratory study that needs to be replicated across larger samples and compared with a healthy control group. CONCLUSIONS: The research contributes to knowledge of the depressive gut microbial profile unique to the anxious distress subtype of MDD.

Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare.

OBJECTIVE: Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls. MATERIALS AND METHODS: Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance. RESULTS: The proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration. DISCUSSION: Our results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research. CONCLUSIONS: This study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

Understanding compensable and non-compensable patient profiles, pathways and physical outcomes for transport and work-related injuries in Queensland, Australia through data linkage.

INTRODUCTION: In many jurisdictions, people experiencing an injury often pursue compensation to support their treatment and recovery expenses. Healthcare costs form a significant portion of payments made by compensation schemes. Compensation scheme regulators need accurate and comprehensive data on injury severity, treatment pathways and outcomes to enable scheme modelling, monitoring and forecasting. Regulators routinely rely on data provided by insurers which have limited healthcare information. Health data provide richer information and linking health data with compensation data enables the comparison of profiles, patterns, trends and outcomes of injured patients who claim and injured parties who are eligible but do not claim. METHODS AND ANALYSIS: This is a retrospective population-level epidemiological data linkage study of people who have sought ambulatory, emergency or hospital treatment and/or made a compensation claim in Queensland after suffering a transport or work-related injury, over the period 1 January 2011 to 31 December 2021. It will use person-linked data from nine statewide data sources: (1) Queensland Ambulance Service, (2) Emergency Department, (3) Queensland Hospital Admitted Patients, (4) Retrieval Services, (5) Hospital Costs, (6) Workers' Compensation, (7) Compulsory Third Party Compensation, (8) National Injury Insurance Scheme and (9) Queensland Deaths Registry. Descriptive, parametric and non-parametric statistical methods and geospatial analysis techniques will be used to answer the core research questions regarding the patient's health service use profile, costs, treatment pathways and outcomes within 2 years postincident as well as to examine the concordance and accuracy of information across health and compensation databases. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Royal Brisbane and Women's Hospital Human Research Ethics Committee, and governance approval was obtained via the Public Health Act 2005, Queensland. The findings of this study will be used to inform key stakeholders across the clinical, research and compensation regulation area, and results will be disseminated through peer-reviewed journals, conference presentations and reports/seminars with key stakeholders.

Changes in prostate specific antigen (PSA) "screening" patterns by geographic region and socio-economic status in Australia: Analysis of medicare data in 50-69 year old men.

BACKGROUND: While it is known that national PSA testing rates have decreased in Australia since 2007, it is not known whether these trends are consistent by broad geographical areas, nor whether previously reported area-specific differences have remained in more recent time periods. METHODS: Population-based cohort study of Australian men (n = 2793,882) aged 50-69 who received at least one PSA test (Medicare Benefit Schedule item number 66655) during 2002-2018. Outcome measures included age-standardised participation rate, annual percentage change using JoinPoint regression and indirectly standardised participation rate ratio using multivariable Poisson regression. RESULTS: During 2005-09, two thirds (68%) of Australian men aged 50-69 had at least one PSA test, reducing to about half (48%) during 2014-18. In both periods, testing rates were highest among men living in major cities, men aged 50-59 years, and among men living in the most advantaged areas. Nationally, the Australian PSA testing rate increased by 9.2% per year between 2002 and 2007, but then decreased by 5.0% per year to 2018. This pattern was generally consistent across States and Territories, and socio-economic areas, however the magnitude of the trends was less pronounced in remote and very remote areas. CONCLUSIONS: The decreasing trends are consistent with a greater awareness of the current guidelines for clinical practice in Australia, which recommend a PSA test be done only with the informed consent of individual men who understand the potential benefits and risks. However, given there remain substantial geographical disparities in prostate cancer incidence and survival in Australia, along with the equivocal evidence for any benefit from PSA screening, there remains a need for more effective diagnostic strategies for prostate cancer to be implemented consistently regardless of where men live.