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Comparative effectiveness of interventions for preventing tuberculosis: systematic review and network meta-analysis of interventional studies

Tuberculosis (TB) is the leading infectious cause of death globally. Several preventive measures are employed to prevent TB, yet there is a paucity of evidence on the effectiveness of these interventions. Therefore, this study aimed to identify the most effective interventions for reducing TB incidence.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts

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.

Core protocol for the adaptive Platform Trial In COVID-19 Vaccine priming and BOOsting (PICOBOO)

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.

Patient-reported outcome measures for paediatric acute lower respiratory infection studies

Patient-reported outcome measures (PROMs) are recommended for capturing meaningful outcomes in clinical trials. The use of PROMs for children with acute lower respiratory infections (ALRIs) has not been systematically reported. We aimed to identify and characterise patient-reported outcomes and PROMs used in paediatric ALRI studies and summarise their measurement properties.

Psychological distress in three Australian communities living with environmental per- and polyfluoroalkyl substances contamination

Environmental chemical contamination is a recognised risk factor for psychological distress, but has been seldom studied in the context of per- and polyfluoroalkyl substances (PFAS) contamination. We examined psychological distress in a cross-sectional study of three Australian communities exposed to PFAS from the historical use of aqueous film-forming foam in firefighting activities, and three comparison communities without environmental contamination.

Influenza and pertussis vaccine coverage in pregnancy in Australia, 2016-2021

Vaccination in pregnancy is the best strategy to reduce complications from influenza or pertussis infection in infants who are too young to be protected directly from vaccination. Pregnant women are also at risk of influenza complications preventable through antenatal vaccination. Both vaccines are funded under the National Immunisation Program for pregnant women in Australia, but coverage is not routinely reported nationally. 

Prevalence of long-term physical sequelae among patients treated with multi-drug and extensively drug-resistant tuberculosis: a systematic review and meta-analysis

Physical sequelae related to multi-drug resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) are emerging and under-recognised global challenges. This systematic review and meta-analysis aimed to quantify the prevalence and the types of long-term physical sequelae associated with patients treated for MDR- and XDR-TB.

Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data

Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination.

Identifying socio-ecological drivers of common cold in Bhutan: a national surveillance data analysis

The common cold is a leading cause of morbidity and contributes significantly to the health costs in Bhutan. The study utilized multivariate Zero-inflated Poisson regression in a Bayesian framework to identify climatic variability and spatial and temporal patterns of the common cold in Bhutan.

among children with pneumonia using a causal Bayesian network

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.