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Sepsis is a significant cause of mortality for children in Australia, particularly affecting young children, those with pre-existing health conditions and Aboriginal and Torres Strait Islander populations. The transition from hospital to home can be challenging for survivors, often leaving long-term impacts unaddressed.
Influenza and COVID-19 infections during pregnancy may have serious adverse consequences for women as well as their infants. However, uptake of influenza and COVID-19 vaccines during pregnancy remains suboptimal. This study aims to assess the effectiveness of a multi-component nudge intervention to improve influenza and COVID-19 vaccine uptake among pregnant women.
Respiratory syncytial virus contributes to significant global infant morbidity and mortality. We applied a previously developed statistical prediction model incorporating pre-pandemic RSV testing data and hospital admission data to estimate infant RSV-hospitalizations by birth month and prematurity, focused on infants aged <1 year.
Increases in invasive group A streptococcal disease (iGAS) have recently been reported in multiple countries in the northern hemisphere, occurring during, and outside of, typical spring peaks. We report the epidemiology of iGAS among children in Australia from 1 July 2018 to 31 December 2022.
The impact of pneumococcal conjugate vaccines (PCVs) on pneumonia in children is well-documented but data on 23-valent pneumococcal polysaccharide vaccine (PPV23) are lacking. Between 2001 and 2011, Indigenous children in Western Australia (WA) were recommended to receive PPV23 at 18-24 months of age following 3 doses of 7-valent PCV. We evaluated the incremental effectiveness of PPV23 against pneumonia hospitalisation.
Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data.
COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.
The need for coronavirus 2019 (COVID-19) vaccination in different age groups and populations is a subject of great uncertainty and an ongoing global debate. Critical knowledge gaps regarding COVID-19 vaccination include the duration of protection offered by different priming and booster vaccination regimens in different populations, including homologous or heterologous schedules.