Background: Understanding mortality burden associated with communicable diseases is key to informing resource allocation, disease prevention and control efforts, and evaluating public health interventions. We quantified excess mortality among people notified with communicable diseases in Victoria, Australia.
Methods: Cases of communicable disease notified in Victoria between 1 January 1991 and 31 December 2021 were linked to the death registry.
Objective: This study explored whether all-cause healthcare attendance rate post-vaccination could detect the two historical influenza safety episodes occurring in 2010 and 2015 using a large de-identified general practitioner (GP) consultations dataset.
Methods: A retrospective observational cohort study was conducted using GP consultation data routinely collected from 2008 to 2017 in Victoria, Australia. Post-vaccination GP consultation rates were monitored, over a 22-week surveillance period each year that aligned with each year's influenza vaccination season, using the Observed minus Expected (O-E) and the Log-Likelihood Ratio (LLR) CUSUM charts.
Background: The increasing availability of electronic healthcare data offers an opportunity to enhance adverse events following immunisation (AEFI) signal monitoring in near real-time.
Aim: To evaluate the potential use of telephone helpline data to augment the existing AEFI surveillance system in Victoria, Australia.
Methods: Anonymised telephone helpline call data were extracted between 2009 and 2017.
Background: SAEFVIC is the Victorian surveillance system for adverse events following immunisation (AEFI). It enhances passive surveillance by also providing clinical support and education to vaccinees and immunisation providers. This report summarises surveillance, clinical and vaccine pharmacovigilance activities of SAEFVIC in 2018.
View Article and Find Full Text PDFIntroduction: Timely adverse event following immunisation (AEFI) signal event detection is essential to minimise further vaccinees receiving unsafe vaccines. We explored the proportional reporting ratio (PRR) ability to detect two known signal events with influenza vaccines with the aim of providing a model for prospective routine signal detection and improving vaccine safety surveillance in Australia.
Methods: Passive AEFI surveillance reports from 2008-2017 relating to influenza vaccines were accessed from the Australian SAEFVIC (Victoria) database.
Background: Concerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.
View Article and Find Full Text PDFCommun Dis Intell (2018)
September 2018
Background: The Bacillus Calmette-Guérin (BCG) vaccine has an important role mitigating tuberculosis (TB) disease in high risk children. In Victoria, immunisation services at the Royal Children's Hospital (RCH) and Monash Health (MH) have been funded as the major providers of BCG vaccine since 2013.
Methods: In this article, we performed retrospective analysis of patients who attended RCH and MH for BCG between 1st November 2013- 30th November 2015.
The ketogenic diet (KD) is a medically supervised, high fat, low carbohydrate and restricted protein diet which has been used successfully in patients with refractory epilepsy. Only one published report has explored its effect on the skeleton. We postulated that the KD impairs skeletal health parameters in patients on the KD.
View Article and Find Full Text PDFIn respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis.
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