Excess mortality among people with communicable diseases over a 30-year period, Victoria, Australia: a whole of population cohort study.

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. Informational gain obtained through linkage and 30-day case fatality rates were calculated for each disease. Standardised mortality ratios (SMR) and 95% confidence intervals were calculated up to a year following illness onset. Findings: There were 1,032,619 cases and 5985 (0.58%) died ≤30 days of illness onset. Following linkage, the 30-day case fatality rate increased more than 2-fold. Diseases with high 7-day SMR signifying excess mortality included invasive pneumococcal disease (167.7, 95% CI 153.4-182.7); listeriosis (166.2, 95% CI 121.2-218.3); invasive meningococcal disease (145.9, 95% CI 116.7-178.3); legionellosis (43.3, 95% CI 28.0-62.0); and COVID-19 (21.9, 95% CI 19.7-24.3). Most diseases exhibited a strong negative gradient, with high SMRs in the first 7-days of illness onset that reduced over time. Interpretation: We demonstrated that the rate of death in Victoria's notifiable disease surveillance dataset is underestimated. Further, compared to a general population, there is evidence of elevated all-cause mortality among people notified with communicable diseases often up to one year following illness onset. Not all elevated risk is likely directly attributable to the communicable diseases of interest, rather, it may reflect underlying comorbidities or behaviours in these individuals. Regardless of attribution, infection with communicable diseases may represent a marker of mortality. Key to preventing deaths may be through timely and appropriate transition to primary and preventive healthcare following diagnosis. Funding: No funding was provided for this study.

Post-vaccination healthcare attendance rate as a proxy measure for syndromic surveillance of adverse events following immunisation.

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. Days 1-7 post-vaccination were considered as the risk period. The LLR CUSUM was designed to detect both a 50% and two-fold rise in the odds of the baseline post-vaccination GP consultation rates. RESULTS: Over the 10-year study period, more than 1.5 million seasonal influenza vaccines doses were administered to 295,091 persons. Overall, 1.29% had a GP consultation within one week of vaccination, but 98.53% of the consultations occurred in days 1-3 post-vaccination. The LLR CUSUM chart detected significant increases in the weekly rates of post-vaccination GP consultation in 2010 in children aged under ten years and in 2015 in adults aged 19-64 years. These increases were aligned by week, but one week earlier and by age category, with the historical adverse events following immunisation (AEFI) signals occurring in 2010 and 2015. However, in the absence of historical AEFI signals, increased rates of post-vaccination GP consultations were identified in three of the eight influenza vaccination years. CONCLUSION: The crude post-vaccination healthcare attendance rate has the potential to offer a sensitive proxy to monitor vaccine safety signal. Implications for public health: Vaccine safety monitoring using syndromic indicator has the potential to augment the existing surveillance systems as part of an integrated vaccine safety monitoring approach.

Use of telephone helpline data for syndromic surveillance of adverse events following immunization in Australia: A retrospective study, 2009 to 2017.

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. For comparison, we included AEFI reports to the Victorian enhanced passive surveillance system, SAEFVIC-"Surveillance of Adverse Events Following Vaccination In the Community". The temporal pattern cross-correlation coefficient at different time lags was estimated as a measure of timeliness evaluation. Historically known AEFI signals in 2010 and 2015 were examined using the Farrington statistical signal detection algorithm. RESULT: During the study period, overall, the telephone helpline centre received 2,005,226 calls. Of these, 0.68% (13,719) were AEFI-related. In the same period, SAEFVIC received 10,367 AEFI related reports. Cross-correlation analysis, generally, showed that the two datasets were moderately correlated (r = 0.4) at a negative lag of 1 week. For individual years, the cross-correlation coefficient was highest (r = 0.66) in 2010 with the telephone helpline data leading by 2 weeks. Our analysis indicated the 2010 reported incidence of febrile convulsions and the 2015 reported increased allergic-related reactions following seasonal influenza vaccination three weeks and one week earlier respectively. CONCLUSION: Telephone helpline data was able to detect an increased rate of AEFI earlier than the enhanced passive AEFI surveillance system. This dataset offers a valuable and near real-time component of an integrated AEFI early signal detection system in Australia.

SAEFVIC: Surveillance of adverse events following immunisation (AEFI) in Victoria, Australia, 2018.

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. METHODS: A retrospective observational cohort study of AEFI reports received by SAEFVIC in 2018, compared with previous years since 2008. Data were categorised by vaccinee demographics of age, sex, pregnancy and Indigenous status, vaccines administered and AEFI reactions reported. Age cohorts were defined as infant (0-12 months); young child (1-4 years); school-aged (5-17 years); adult (18-64 years); and older person (65+ years). Proportional reporting ratios were calculated for signal investigation of serious adverse neurological events with all vaccines and with influenza vaccines. Clinical support services and educational activities are described. RESULTS: SAEFVIC received 1730 AEFI reports (26.8 per 100,000 population), with 9.3% considered serious. Nineteen percent (n = 329) attended clinical review. Annual AEFI reporting trends increased for infants, children and older persons, but were stable for school-aged and adult cohorts. Females comprised 55% of all reports and over 80% of reports among adults. There were 17 reports of AEFI in pregnant women and 12 (0.7%) in persons identifying as Indigenous Australians. A possible signal regarding serious adverse neurological events (SANE) was detected, but was not supported by signal validation testing. A clinical investigation is ongoing. Two deaths were reported coincident to immunisation with no evidence of causal association. CONCLUSION: SAEFVIC continues to provide robust AEFI surveillance supporting vaccine safety monitoring in Victoria and Australia, with new signal detection and validation methodologies strengthening capabilities.

Early signal detection of adverse events following influenza vaccination using proportional reporting ratio, Victoria, Australia.

INTRODUCTION: 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. Proportional reporting ratios were calculated for two vaccine-event categories; fever and allergic AEFI. Signal detection sensitivity for two known signal events were determined using weekly data; cumulative data by individual year and; cumulative for all previous years. Signal event thresholds of PRR ≥2 and Chi-square ≥4 were applied. RESULTS: PRR provided sensitive signal detection when calculated cumulatively by individual year or by all previous years. Known signal events were detected 15 and 11 days earlier than traditional methods used at the time of the actual events. CONCLUSION: Utilising a single jurisdiction's data, PRR improved vaccine pharmacovigilance and showed the potential to detect important safety signals much earlier than previously. It has potential to maximise immunisation safety in Australia. This study progresses the necessary work to establish national cohesion for passive surveillance signal detection and strengthen routine Australian vaccine pharmacovigilance.

Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review.

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. METHODS: We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively. RESULT: We included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time. CONCLUSION: Routinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection. TRIAL REGISTRATION NUMBER: CRD42017072741.

Distribution of Bacillus Calmette-Guérin (BCG) Vaccine in Victoria 2013-2015.

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. This was compared with local birth data in order to portray the distribution of BCG vaccine across various cohorts. OUTCOMES: A total of 3,975 patients received BCG vaccine (1,775 at Monash, 2,200 from RCH). Detailed data is only available on 830 RCH patients. The median age of the study population was 6.9 months (IQR 3.9-11.3). The majority of children (98.9%, 2,575/2,604) received BCG vaccine prior to overseas travel. Of these, 96.0% (2,474/2,575) were travelling to countries in Asia. Only 13/2,604 (0.5%) were given BCG vaccine prior to travel to a country with low incidence of TB. Most infants were of Asian descent (93.3% mothers [2,425/2,604], 90.4% [2,346/2,604] fathers). A much smaller proportion was African (1.4% mothers [35/2,604], 1.5% [39/2,604] fathers). This contrasts with 2012 Victorian birth data, which showed that 82.2% (7,508/ 9,134) babies born to mothers from high TB prevalence countries were of Asian descent, whereas 8.9% (816/ 9,134) were of African descent. These results highlight scope to improve awareness and equity of BCG vaccine service, particularly to infants of African background.

The effect of the ketogenic diet on the developing skeleton.

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. Patients commenced on the KD were enrolled in a prospective, longitudinal study, with monitoring of Dual-energy X-ray absorptiometry (DXA) derived bone parameters including bone mineral content and density (BMD). Areal BMD was converted to bone mineral apparent density (BMAD) where possible. Biochemical parameters, including Vitamin D, and bone turnover markers, including osteocalcin, were assessed. Patients were stratified for level of mobility using the gross motor functional classification system (GMFCS). 29 patients were on the KD for a minimum of 6 months (range 0.5-6.5 years, mean 2.1 years). There was a trend towards a reduction in lumbar spine (LS) BMD Z score of 0.1562 (p=0.071) per year and 20 patients (68%) had a lower BMD Z score at the end of treatment. While less mobile patients had lower baseline Z scores, the rate of bone loss on the diet was greater in the more mobile patients (0.28 SD loss per year, p=0.026). Height adjustment of DXA data was possible for 13 patients, with a mean reduction in BMAD Z score of 0.19 SD. Only two patients sustained fractures. Mean urinary calcium-creatinine ratios were elevated (0.77), but only 1 patient developed renal calculi. Children on the KD exhibited differences in skeletal development that may be related to the diet. The changes were independent of height but appear to be exaggerated in patients who are ambulant. Clinicians should be aware of potential skeletal side effects and monitor bone health during KD treatment. Longer term follow up is required to determine adult/peak bone mass and fracture risk throughout life.

Introduction to causal diagrams for confounder selection.

In 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. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables. Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder-selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables. After creating a causal diagram for the research question, an intuitive and easy-to-use set of rules can be applied, based on a foundation of rigorous mathematics, to decide which measured variables must be controlled for in the statistical analysis in order to remove confounding, to the extent that is possible using the available data. This approach is illustrated by constructing a causal diagram for the research question: 'Does personal smoking affect the risk of subsequent asthma?'. Using data taken from the Tasmanian Longitudinal Health Study, the statistical analysis suggested by the causal diagram approach was performed.