Epidemiology of repeat influenza infection in Queensland, Australia, 2005-2017.

Natural infection with the influenza virus is believed to generate cross-protective immunity across both types and subtypes. However, less is known about the persistence of this immunity and thus the susceptibility of individuals to repeat infection. We used 13 years (2005-2017) of surveillance data from Queensland, Australia, to describe the incidence and distribution of repeat influenza infections. Consecutive infections that occurred within 14 days of prior infection were considered a mixed infection; those that occurred more than 14 days later were considered separate (repeat) infections. Kaplan-Meier plots were used to investigate the probability of reinfection over time and the Prentice, Williams and Peterson extension of the Cox proportional hazards model was used to assess the association of age and gender with reinfection. Among the 188 392 notifications received during 2005-2017, 6165 were consecutively notified for the same individual (3.3% of notifications), and 2958 were mixed infections (1.6%). Overall, the probability of reinfection was low: the cumulative incidence was <1% after one year, 4.6% after five years, and 9.6% after ten years. The majority of consecutive infections were the result of two type A infections (43%) and were most common among females (adjusted hazard ratio (aHR): 1.15, 95% confidence interval (CI) 1.09-1.21), children aged less than 5 years (relative to adults aged 18-64 years aHR: 1.58, 95% CI 1.47-1.70) and older adults aged at least 65 years (aHR: 1.35; 95% CI 1.24-1.47). Our study suggests consecutive infections are possible but rare. These findings have implications for our understanding of population immunity to influenza.

Impact of COVID-19 Mitigation Measures on Mosquito-Borne Diseases in 2020 in Queensland, Australia.

We describe the impact of COVID-19 mitigation measures on mosquito-borne diseases in Queensland, Australia, during the first half of 2020. Implementation of restrictions coincided with an atypical late season outbreak of Ross River virus (RRV) characterized by a peak in notifications in April (1173) and May (955) which were greater than 3-fold the mean observed for the previous four years. We propose that limitations on human movement likely resulted in the majority of RRV infections being acquired at or near the place of residence, and that an increase in outdoor activities, such as gardening and bushwalking in the local household vicinity, increased risk of exposure to RRV-infected mosquitoes. In contrast, the precipitous decline in international passenger flights led to a reduction in the number of imported dengue and malaria cases of over 70% and 60%, respectively, compared with the previous five years. This substantial reduction in flights also reduced a risk pathway for importation of exotic mosquitoes, but the risk posed by importation via sea cargo was not affected. Overall, the emergence of COVID-19 has had a varied impact on mosquito-borne disease epidemiology in Queensland, but the need for mosquito surveillance and control, together with encouragement of personal protective measures, remains unchanged.

Anatomy of a seasonal influenza epidemic forecast

Bayesian methods have been used to predict the timing of infectious disease epidemics in various settings and for many infectious diseases, including seasonal influenza. But integrating these techniques into public health practice remains an ongoing challenge, and requires close collaboration between modellers, epidemiologists, and public health staff. During the 2016 and 2017 Australian influenza seasons, weekly seasonal influenza forecasts were produced for cities in the three states with the largest populations: Victoria, New South Wales, and Queensland. Forecast results were presented to Health Department disease surveillance units in these jurisdictions, who provided feedback about the plausibility and public health utility of these predictions. In earlier studies we found that delays in reporting and processing of surveillance data substantially limited forecast performance, and that incorporating climatic effects on transmission improved forecast performance. In this study of the 2016 and 2017 seasons, we sought to refine the forecasting method to account for delays in receiving the data, and used meteorological data from past years to modulate the force of infection. We demonstrate how these refinements improved the forecast's predictive capacity, and use the 2017 influenza season to highlight challenges in accounting for population and clinician behaviour changes in response to a severe season.

The changing phases of pandemic (H1N1) 2009 in Queensland: an overview of public health actions and epidemiology.

A graded public health response was implemented to control the pandemic (H1N1) 2009 outbreak in Queensland. Public health measures to contain the outbreak included border control, enhanced surveillance, management of cases and contacts with isolation or quarantine and antivirals, school closures and public education messages. The first confirmed case in Australia was notified on 8 May 2009, in a traveller returning to Queensland from the United States. In Queensland, 593 laboratory-confirmed cases were notified with a date of onset between 26 April and 22 June 2009, when the Protect phase of the Australian Health Management Plan for Pandemic Influenza was implemented; 16 hospitalisations and no deaths were reported during this time. The largest number of confirmed cases was reported in the 10-19-years age group (167, 28% of cases), followed by the 20-29-years age group (153, 26% of cases). With ongoing community transmission, the focus has shifted from public health to the clinical domain, with an emphasis on protecting vulnerable groups. Considerable resources have been invested to prevent and control the spread of disease in Indigenous communities in Far North Queensland. The capacity of clinical services to cope with increased admissions, the potential for widespread antiviral resistance, and rollout of mass vaccination campaigns remain future challenges.

Early evidence for direct and indirect effects of the infant rotavirus vaccine program in Queensland.

OBJECTIVE: To assess the impact of introducing a publicly funded infant rotavirus vaccination program on disease notifications and on laboratory testing and results. DESIGN AND SETTING: Retrospective analysis of routinely collected data (rotavirus notifications [2006-2008] and laboratory rotavirus testing data from Queensland Health laboratories [2000-2008]) to monitor rotavirus trends before and after the introduction of a publicly funded infant rotavirus vaccination program in Queensland in July 2007. MAIN OUTCOME MEASURES: Age group-specific rotavirus notification trends; number of rotavirus tests performed and the proportion positive. RESULTS: In the less than 2 years age group, rotavirus notifications declined by 53% (2007) and 65% (2008); the number of laboratory tests performed declined by 3% (2007) and 15% (2008); and the proportion of tests positive declined by 45% (2007) and 43% (2008) compared with data collected before introduction of the vaccination program. An indirect effect of infant vaccination was seen: notifications and the proportion of tests positive for rotavirus declined in older age groups as well. CONCLUSIONS: The publicly funded rotavirus vaccination program in Queensland is having an early impact, direct and indirect, on rotavirus disease as assessed using routinely collected data. Further observational studies are required to assess vaccine effectiveness. Parents and immunisation providers should ensure that all Australian children receive the recommended rotavirus vaccine doses in the required timeframe.

Modeling the economic losses from pressure ulcers among hospitalized patients in Australia.

The objective of this study was to predict the number of cases of pressure ulcer, the bed days lost, and the economic value of these losses at Australian public hospitals. All adults (>or= 18 years of age) with a minimum stay of 1 night and discharged from selected clinical units from all Australian public hospitals in 2001-02 were included in the study. The main outcome measures were the number of cases of pressure ulcer, bed days lost to pressure ulcer, and economic value of these losses. We predict a median of 95,695 cases of pressure ulcer with a median of 398,432 bed days lost, incurring median opportunity costs of AU$285 M. The number of cases, and so costs, were greatest in New South Wales and lowest in Australian Capitol Territory. We conclude that pressure ulcers represent a serious clinical and economic problem for a resource-constrained public hospital system. The most cost-effective, risk-reducing interventions should be pursued up to a point where the marginal benefit of prevention is equalized with marginal cost. By preventing pressure ulcers, public hospitals can improve efficiency and the quality of the patient's experience and health outcome.