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Charities play an increasingly important role in helping people experiencing poverty. However, institutionalized charity shifts the burden of poverty reduction away from the state and exposes recipients to stress and stigma. In this paper, we examine whether the need for institutionalized charity can be offset through enhanced state support. As in other countries, the Australian government responded to the COVID-19 pandemic by substantially increasing the level of income support to citizens through several temporary payments. We draw on this natural experiment and time-series data from the two largest charity organizations in Queensland, Australia to examine how these payments altered the demand for institutionalized charity. We model these data using difference-in-difference regression models to approximate causal effects. By exploiting the timing and varying amounts of the payments, our analyses yield evidence that more generous income support reduces reliance on charity. Halving the demand for charity requires raising pre-pandemic income-support by AUD$42/day, with supplements of approximately AUD$18/day yielding the greatest return on investment. [ABSTRACT FROM AUTHOR]

This study aimed to better understand the vulnerability of children in their first year of school, aged between 5 years 5 months and 6 years 6 months, based on five health and development domains. Identification of subgroups of children within these domains can lead to more targeted policies to reduce these vulnerabilities. The focus of this study was to determine clusters of geographical regions with high and low proportions of vulnerable children in Queensland, Australia. This was achieved by carrying out a K-means analysis on data from the Australian Early Development Census and the Australian Bureau of Statistics. The clusters were then compared with respect to their geographic locations and risk factor profiles. The results are made publicly available via an interactive dashboard application developed in R Shiny. [ABSTRACT FROM AUTHOR]

Background: Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. Methodology/Principal Findings: We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. Conclusions/Significance: A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control. [ABSTRACT FROM AUTHOR]

Objective: To redress the lack of Queensland population incidence mortality and morbidity data associated with drowning in those aged 0-19yrs, and to understand survival and patient care. Design, Setting and Participants: Retrospective population-based study used data linkage to capture both fatal and non-fatal drowning cases (N = 1299) among children aged 0-19years in Queensland, from 2002-2008 inclusive. Patient data were accessed from pre-hospital, emergency department, hospital admission and death data, and linked manually to collate data across the continuum of care. Main Outcome Measures: Incidence rates were calculated separately by age group and gender for events resulting in death, hospital admission, and non-admission. Trends over time were analysed. Results: Drowning death to survival ratio was 1:10, and two out of three of those who survived were admitted to hospital. Incidence rates for fatal and non-fatal drowning increased over time, primarily due to an increase in non-fatal drowning. There were non-significant reductions in fatal and admission rates. Rates for non-fatal drowning that did not result in hospitalisation more than doubled over the seven years. Children aged 5-9yrs and 10-14yrs incurred the lowest incidence rates 6.38 and 4.62 (expressed as per 100,000), and the highest rates were among children aged 0-4yrs (all drowning events 43.90; fatal 4.04; non-fatal 39.85–comprising admission 26.69 and non-admission 13.16). Males were over-represented in all age groups except 10-14yrs. Total male drowning events increased 44% over the seven years (P<0.001). Conclusion: This state-wide data collection has revealed previously unknown incidence and survival ratios. Increased trends in drowning survival rates may be viewed as both positive and challenging for drowning prevention and the health system. Males are over-represented, and although infants and toddlers did not have increased fatality rates, they had the greatest drowning burden demonstrating the need for continued drowning prevention efforts. [ABSTRACT FROM AUTHOR]