Below we showcase several projects in which SIH has delivered a contribution to a paper. See all projects.
Meta-analysis into Adolescent Oral Health Interventions
Oral health promotion for younger-aged children is more widely conducted and better understood than that directed at adolescents. The aim of this systematic review was to evaluate the effectiveness of oral health interventions in improving the knowledge, attitudes, behaviour and oral health status of healthy adolescents. We looked at gingival health, plaque levels, and dental caries within randomized controlled trials, as reported in the literature. The interventions reported ranged from single session interventions to community-wide programs with many also including clinical preventive procedures and take-home products. Half of the programs used a health behaviour change theory to inform their intervention. The meta-analysis showed an improvement in all three of gingival score, plaque score and the number of decayed missing and filled tooth surfaces after an oral health promotion intervention, and with respect to a control group that did not receive the intervention.
Discharge Against Medical Advice in Culturally and Linguistically Diverse Patients
In this study we examined discharge against medical advice (DAMA) and its relation to the cultural and linguistic diversity (CALD) of 600,000 patients over 9 years in the Sydney Children’s Hospital Network. Using a bayesian logistic regression framework, we found CALD status to be significantly positively correlated with DAMA rates. Identification of this link provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes for CALD patients.
Applying Machine Learning to Criminology
The incidence of crime the impacts of societal and individual characteristics on criminal behaviour can be explored using modern machine learning methods, answering important questions about crime, such as: • What is the probability of a crime occurring at a location? • What are the characteristics of the population that affect the incidence of crime? Our work implements novel Bayesian machine learning techniques to modelling the dependency between offence data and demographic characteristics and spatial location. This provides a fully probabilistic approach to modelling crime which reflects all uncertainties in the prediction of offences as well as the uncertainties surrounding model parameters. By using Bayesian updating, these predictions and inferences are dynamic in the sense that they change as new information becomes available. Our model has been applied to offence data, such as domestic violence-related assaults, burglary and motor vehicle theft, in New South Wales (NSW), Australia. The results highlight the strength of the technique by validating the factors that are associated with high and low criminal activity.
Discharge against medical advice in the Sydney Children's Hospital Network
Patients who discharge against medical advice (DAMA) from hospital carry a significant risk of readmission and have increased rates of morbidity and mortality. Using five years of admissions and diagnosis data, we sought to identify the demographic, clinical and administrative characteristics of DAMA patients in the Sydney Children’s Hospital Network. Using a bayesian logistic regression framework, we found statistically significant predictors of DAMA in a given admission were hospital site, a mental health/behavioural diagnosis, Aboriginality, emergency rather than elective admissions, a gastrointestinal diagnosis and a history of previous DAMA. Identification of these predictors of DAMA provides opportunities for intervention at a practice and policy level in order to prevent adverse outcomes for patients.
Disease spectrum and management of children admitted with acute respiratory infection in Viet Nam
- Nguyen Thi Kim Phuong, Respiratory Department, Da Nang Hospital for Women and Children; Professor Ben Marais, The Children’s Hospital at Westmead Clinical School and Deputy Director, Marie Bashir Institute for Infectious Diseases and Biosecurity
- Faculty of Health Sciences
- Data Science (Dr Maryam Montazerolghaem )
- Description and basic visualization
This study aim to assess the acute respiratory infection (ARI) disease spectrum, duration of hospitalisation and outcome in children hospitalised with an ARI in Viet Nam. The result indicates that acute respiratory infection is a major cause of paediatric hospitalisation in Viet Nam, characterised by prolonged hospitalisation for relatively mild disease. There is huge potential to reduce unnecessary hospital admission and cost.