Chart and table of population level and growth rate for the Gold Coast-Tweed Head, Australia metro area from 1950 to 2025.

The Queensland Gay Community Periodic Survey is a cross-sectional survey of gay and homosexually active men recruited through a range of gay community sites in QLD. Data were collected on types of sexual relationships and number of partners, anal and oral intercourse, unprotected anal intercourse, testing for HIV and other STIs, HIV serostatus, recreational drug use, as well as demographic characteristics such as sexual identity and age. Sample Population: gay and homosexually-active men from Queensland. Method of Data Collection: Self-completion. Participants were recruited through sites in Brisbane, the Gold Coast, the Sunshine Coast, Cairns and Townville as well as from gay social venues, gay sex-on-premises venues, sexual health clinics and the Pride Fair Day. Kind of Data: Survey. Sampling Procedures: Convenience sample. Time Dimensions: Repeated cross-sectional study.
http://csrh.arts.unsw.edu.au/research/publications/gcps/

Births that occurred by hospital name. Birth events of 5 or more per hospital location are displayed

The Australian Urban Health Indicators (AusUrb-HI) project, a collaboration between NCRIS facilities, AURIN, PHRN, and researchers, aims to improve understanding of urban and regional health. This dataset, developed with Cancer Council Queensland and the Australian Cancer Atlas, provides spatial indicators of breast cancer outcomes in Queensland, exploring associations between various determinants and cancer patterns. The dataset’s goal is to identify spatial variations in breast cancer outcomes within Queensland, and motivate further research into their drivers. This dataset includes seven spatial indicators of breast cancer outcomes for Queensland women, modeled using Bayesian statistics, covering the period of 2000-2019. These indicators, presented as Standardized Incidence Ratios (SIRs) with 95% credible intervals and posterior probability differences (ppd), are at the Statistical Area Level 2 (SA2). The dataset provides modelled statistical indicators and geographic data, covering breast cancer outcomes, spatial epidemiology, health disparities, and cancer surveillance at the SA2 level (2016 ASGS) in Queensland, Australia. The intended application is for identifying geographical disparities in breast cancer, informing public health planning, and enabling further research. The methodology uses linked health datasets from the Queensland Cancer Register, BreastScreen Queensland, and the Queensland Hospital Admitted Patients Data Collection. Bayesian spatial models with Besag-Yorke-Mollié (BYM) priors were applied, using a Poisson distribution with the log of expected counts as an offset, and incorporating spatial terms for neighboring regions. Models were validated using trace plots, density plots, Moran’s I statistics, and Geweke statistics. The data is structured as a table, with each row representing an SA2 region, and columns representing indicator values and associated metrics. The complete attribute dictionary for the dataset is shown below:

sa2: Five digital sa2 code using 2016 ASGS sa2name: Name of each sa2 area

urban: Categorical variable with three levels: 1. brisbane/gold coast; 2. Cairns; 3. Rest of Queensland, with the first two representing the urban areas in Queensland. Categorised based on sa3 name.

sir_incidence: Standardised incidence ratios (SIR) showing the variation in incidence of breast cancer. SIR is using the total Queensland cohort as the reference population.

low_cri_incidence: The lower bound of the 95% credible interval for the sir_incidence. high_cri_incidence: The higher bound of the 95% credible interval for the sir_incidence.

ppd_incidence: The posterior probability difference of sir_incidence outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_localised: Standardised incidence ratios (SIR) showing the variation in proportion of invasive breast cancers that were diagnosed when localised. SIR is using the total Queensland cohort as the reference population.

low_cri_localised: The lower bound of the 95% credible interval for the sir_localised. high_cri_localised: The higher bound of the 95% credible interval for the sir_localised.

ppd_localised: The posterior probability difference of sir_localised outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_survive5yrs: Standardised incidence ratios (SIR) showing the variation in surviving at least five years among women with a diagnosis of invasive breast cancer. SIR is using the total Queensland cohort as the reference population.

low_cri_survive5yrs: The lower bound of the 95% credible interval for the sir_survive5yrs. high_cri_survive5yrs: The higher bound of the 95% credible interval for the sir_survive5yrs.

ppd_survive5yrs: The posterior probability difference of sir_survive5yrs outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_hospitalization: Standardised incidence ratios (SIR) showing the variation in hospitalization rates among women with a diagnosis of invasive breast cancer. SIR is using the total Queensland cohort as the reference population.

low_cri_hospitalization: The lower bound of the 95% credible interval for the sir_hospitalization. high_cri_hospitalization: The higher bound of the 95% credible interval for the sir_hospitalization.

ppd_hospitalization: The posterior probability difference of sir_hospitalization outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_surgery: Standardised incidence ratios (SIR) showing the variation in rates of breast cancer surgery among women with a diagnosis of invasive breast cancer. SIR is using the total Queensland cohort as the reference population.

low_cri_surgery: The lower bound of the 95% credible interval for the sir_surgery. high_cri_surgery: The higher bound of the 95% credible interval for the sir_surgery.

ppd_surgery: The posterior probability difference of sir_surgery outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_screen: Standardised incidence ratios (SIR) showing the variation in prevalence of regular breast cancer screening among women with a diagnosis of invasive breast cancer, SIR is using the total Queensland cohort as the reference population.

low_cri_screen: The lower bound of the 95% credible interval for the sir_screen. high_cri_screen: The higher bound of the 95% credible interval for the sir_screen.

ppd_screen: The posterior probability difference of sir_screen outcome, with ppd>0.6 showing a significant difference from the Queensland average.

sir_optexp: Standardised incidence ratios (SIR) showing the variation in optimal breast cancer experience among women with a diagnosis of invasive breast cancer. SIR is using the total Queensland cohort as the reference population.

low_cri_optexp: The lower bound of the 95% credible interval for the sir_optexp. high_cri_optexp: The higher bound of the 95% credible interval for the sir_optexp. ppd_optexp: The posterior probability difference of sir_optexp outcome, with ppd>0.6 showing a significant difference from the Queensland average.

The region of coastal South East Queensland (SEQ) represents a large concentration of population, business activity and infrastructure important to the economy of Queensland and Australia. The region is also subject to severe storms that can generate damaging winds, particularly as a result of thunderstorm and tropical cyclone activity. Older residential homes have historically been the most damaged in such storms, contributing disproportionately to community risk, and recent storm damage in Western Australia has indicated that there are issues with modern SEQ homes also. This risk posed by severe wind is not well understood, nor are the optimal strategies for managing and potentially reducing this risk. Previous work has provided insights into the potential impacts of rare storm events in the SEQ region and the vulnerability of residential homes that contribute to them. The Severe Wind Hazard Assessment for Queensland (SWHAQ) project (Arthur, et al., 2021) provided valuable insights on the potential impacts of rare tropical cyclones making landfall in the region. The SWHA-Q project included two storms impacting the Gold Coast that highlighted that credible cyclone events in South East Queensland generating no more than design level wind gusts can have challenging consequences.

Five tropical cyclone scenario events were selected by the project partners and modelled to provide a demonstration of the residential housing damage outcomes that could result from plausible storms that could impact South East Queensland. Four storms generated category 3 winds (gusts over 165 km/h) on landfall and were essentially design level events for ordinary residential structures. The fifth (Scenario 3) generated category 4 winds (gusts over 225 km/h) at landfall but was still quite a credible storm for the region. The events highlighted, as did the previous SWHA-Q work, that rare cyclone events of this kind affect all parts of the study region and produce very significant consequences. One design level event (Scenario 2) was found to inflict moderate or greater damage to 39% of the homes in the region, representing a major need for temporary accommodation. One of the events was used as the evidence-based scenario that underpinned Exercise Averruncus – A SEQ Tropical Cyclone Impact held in Brisbane on 15 June 2022 that explored critical issues around preparation for, response to, and initial recovery from the event. It is noted that the scale of impacts from any scenario is contingent on the characteristics of the TC itself (size, intensity, landfall location) and on the landscape in which buildings are located. However, while each scenario is unique, the suite of scenario impacts provide a useful resource for EM planning by local government, emergency services and other agencies with a role in disaster recovery.