The high burden of infectious disease and associated antimicrobial use likely contribute to the emergence of antimicrobial resistance in remote Australian Aboriginal communities. We aimed to develop ...and apply context-specific tools to audit antimicrobial use in the remote primary healthcare setting.
We adapted the General Practice version of the National Antimicrobial Prescribing Survey (GP NAPS) tool to audit antimicrobial use over 2-3 weeks in 15 remote primary healthcare clinics across the Kimberley region of Western Australia (03/2018-06/2018), Top End of the Northern Territory (08/2017-09/2017) and far north Queensland (05/2018-06/2018). At each clinic we reviewed consecutive clinic presentations until 30 presentations where antimicrobials had been used were included in the audit. Data recorded included the antimicrobials used, indications and treating health professional. We assessed the appropriateness of antimicrobial use and functionality of the tool.
We audited the use of 668 antimicrobials. Skin and soft tissue infections were the dominant treatment indications (WA: 35%; NT: 29%; QLD: 40%). Compared with other settings in Australia, narrow spectrum antimicrobials like benzathine benzylpenicillin were commonly given and the appropriateness of use was high (WA: 91%; NT: 82%; QLD: 65%). While the audit was informative, non-integration with practice software made the process manually intensive.
Patterns of antimicrobial use in remote primary care are different from other settings in Australia. The adapted GP NAPS tool functioned well in this pilot study and has the potential for integration into clinical care. Regular stewardship audits would be facilitated by improved data extraction systems.
•Pandemic influenza plans recognise the importance of situational assessment.•First few X studies and algorithms enable early assessment of pandemic impact.•We present a decision support tool linking ...such analytics to response policy.•The tool focuses on quantifying decision certainty rather than scenario certainty.
National influenza pandemic plans have evolved substantially over recent decades, as has the scientific research that underpins the advice contained within them. While the knowledge generated by many research activities has been directly incorporated into the current generation of pandemic plans, scientists and policymakers are yet to capitalise fully on the potential for near real-time analytics to formally contribute to epidemic decision-making. Theoretical studies demonstrate that it is now possible to make robust estimates of pandemic impact in the earliest stages of a pandemic using first few hundred household cohort (FFX) studies and algorithms designed specifically for analysing FFX data. Pandemic plans already recognise the importance of both situational awareness i.e., knowing pandemic impact and its key drivers, and the need for pandemic special studies and related analytic methods for estimating these drivers. An important next step is considering how information from these situational assessment activities can be integrated into the decision-making processes articulated in pandemic planning documents. Here we introduce a decision support tool that directly uses outputs from FFX algorithms to present recommendations on response options, including a quantification of uncertainty, to decision makers. We illustrate this approach using response information from within the Australian influenza pandemic plan.
For in vivo studies of influenza dynamics where within-host measurements are fit with a mathematical model, infectivity assays (e.g. 50% tissue culture infectious dose; TCID50) are often used to ...estimate the infectious virion concentration over time. Less frequently, measurements of the total (infectious and non-infectious) viral particle concentration (obtained using real-time reverse transcription-polymerase chain reaction; rRT-PCR) have been used as an alternative to infectivity assays. We investigated the degree to which measuring both infectious (via TCID50) and total (via rRT-PCR) viral load allows within-host model parameters to be estimated with greater consistency and reduced uncertainty, compared with fitting to TCID50 data alone. We applied our models to viral load data from an experimental ferret infection study. Best-fit parameter estimates for the "dual-measurement" model are similar to those from the TCID50-only model, with greater consistency in best-fit estimates across different experiments, as well as reduced uncertainty in some parameter estimates. Our results also highlight how variation in TCID50 assay sensitivity and calibration may hinder model interpretation, as some parameter estimates systematically vary with known uncontrolled variations in the assay. Our techniques may aid in drawing stronger quantitative inferences from in vivo studies of influenza virus dynamics.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We introduce a novel mathematical model that effectively incorporates contact tracing in a realistic distribution mechanism for antiviral drugs in an influenza pandemic scenario. A strategy focused ...on targeted provision of post-exposure prophylaxis, rather than treatment, will provide the greatest chance of minimising the impact of an influenza pandemic. Targeted post-exposure prophylaxis delays the onset of the pandemic and for a wide range of parameter values, a delay of the order of 6–18 months may be achievable. This may provide enough time to develop and distribute a vaccine. In contrast, a treatment based strategy typically does not delay the onset of a pandemic by an appreciable amount and, in general, is not capable of significantly reducing the attack rate from baseline.
Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious ...diseases, climate change and the biodiversity crisis. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decision-making process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.
Households are known to be high-risk locations for the transmission of communicable diseases. Numerous modelling studies have demonstrated the important role of households in sustaining both ...communicable diseases outbreaks and endemic transmission, and as the focus for control efforts. However, these studies typically assume that households are associated with a single dwelling and have static membership. This assumption does not appropriately reflect households in some populations, such as those in remote Australian Aboriginal and Torres Strait Islander communities, which can be distributed across more than one physical dwelling, leading to the occupancy of individual dwellings changing rapidly over time. In this study, we developed an individual-based model of an infectious disease outbreak in communities with demographic and household structure reflective of a remote Australian Aboriginal community. We used the model to compare the dynamics of unmitigated outbreaks, and outbreaks constrained by a household-focused prophylaxis intervention, in communities exhibiting fluid vs. stable dwelling occupancy. We found that fluid dwelling occupancy can lead to larger and faster outbreaks in modelled scenarios, and may interfere with the effectiveness of household-focused interventions. Our findings suggest that while short-term restrictions on movement between dwellings may be beneficial during outbreaks, in the longer-term, strategies focused on reducing household crowding may be a more effective way to reduce the risk of severe outbreaks occurring in populations with fluid dwelling occupancy.
The clinical attack rate of influenza is influenced by prior immunity and mixing patterns in the host population, and also by the proportion of infections that are asymptomatic. This complexity makes ...it difficult to directly estimate R(0) from the attack rate, contributing to uncertainty in epidemiological models to guide pandemic planning. We have modelled multiple wave outbreaks of influenza from different populations to allow for changing immunity and asymptomatic infection and to make inferences about R(0).
On the island of Tristan da Cunha (TdC), 96% of residents reported illness during an H3N2 outbreak in 1971, compared with only 25% of RAF personnel in military camps during the 1918 H1N1 pandemic. Monte Carlo Markov Chain (MCMC) methods were used to estimate model parameter distributions.
We estimated that most islanders on TdC were non-immune (susceptible) before the first wave, and that almost all exposures of susceptible persons caused symptoms. The median R(0) of 6.4 (95% credibility interval 3.7-10.7) implied that most islanders were exposed twice, although only a minority became ill in the second wave because of temporary protection following the first wave. In contrast, only 51% of RAF personnel were susceptible before the first wave, and only 38% of exposed susceptibles reported symptoms. R(0) in this population was also lower 2.9 (2.3-4.3), suggesting reduced viral transmission in a partially immune population.
Our model implies that the RAF population was partially protected before the summer pandemic wave of 1918, arguably because of prior exposure to interpandemic influenza. Without such protection, each symptomatic case of influenza would transmit to between 2 and 10 new cases, with incidence initially doubling every 1-2 days. Containment of a novel virus could be more difficult than hitherto supposed.
Effective response to emerging infectious disease (EID) threats relies on health care systems that can detect and contain localised outbreaks before they reach a national or international scale. The ...Asia-Pacific region contains low and middle income countries in which the risk of EID outbreaks is elevated and whose health care systems may require international support to effectively detect and respond to such events. The absence of comprehensive data on populations, health care systems and disease characteristics in this region makes risk assessment and decisions about the provision of such support challenging.
We describe a mathematical modelling framework that can inform this process by integrating available data sources, systematically explore the effects of uncertainty, and provide estimates of outbreak risk under a range of intervention scenarios. We illustrate the use of this framework in the context of a potential importation of Ebola Virus Disease into the Asia-Pacific region. Results suggest that, across a wide range of plausible scenarios, preemptive interventions supporting the timely detection of early cases provide substantially greater reductions in the probability of large outbreaks than interventions that support health care system capacity after an outbreak has commenced.
Our study demonstrates how, in the presence of substantial uncertainty about health care system infrastructure and other relevant aspects of disease control, mathematical models can be used to assess the constraints that limited resources place upon the ability of local health care systems to detect and respond to EID outbreaks in a timely and effective fashion. Our framework can help evaluate the relative impact of these constraints to identify resourcing priorities for health care system support, in order to inform principled and quantifiable decision making.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Households are an important location for the transmission of communicable diseases. Social contact between household members is typically more frequent, of greater intensity, and is more likely to ...involve people of different age groups than contact occurring in the general community. Understanding household structure in different populations is therefore fundamental to explaining patterns of disease transmission in these populations. Indigenous populations in Australia tend to live in larger households than non-Indigenous populations, but limited data are available on the structure of these households, and how they differ between remote and urban communities. We have developed a novel approach to the collection of household structure data, suitable for use in a variety of contexts, which provides a detailed view of age, gender, and room occupancy patterns in remote and urban Australian Indigenous households. Here we report analysis of data collected using this tool, which quantifies the extent of crowding in Indigenous households, particularly in remote areas. We use these data to generate matrices of age-specific contact rates, as used by mathematical models of infectious disease transmission. To demonstrate the impact of household structure, we use a mathematical model to simulate an influenza-like illness in different populations. Our simulations suggest that outbreaks in remote populations are likely to spread more rapidly and to a greater extent than outbreaks in non-Indigenous populations.
As control efforts progress towards elimination, malaria is likely to become more spatially concentrated in few local areas. The purpose of this study was to quantify and characterise spatial ...heterogeneity in malaria transmission-intensity across highly endemic Indonesian Papua.
We analysed individual-level malaria surveillance data for nearly half a million cases (2019–2020) reported in the Papua and West Papua provinces and adapted the Gini index approach to quantify spatial heterogeneity at the district and health-unit levels. In this context, high Gini index implies disproportionately distributed malaria cases across the region. We showed malaria incidence trends and the spatial and temporal distribution of sociodemographic characteristics and aetiological parasites among cases.
While Papua province accounted for the majority of malaria cases reported in the region and had seen a rise in transmission since 2015, West Papua province had maintained a comparatively low incidence. We observed that Gini index estimates were high, particularly when the lower spatial scale of health units was evaluated. The Gini index appears to be inversely associated to annual parasite-incidence, as well as the proportions of vivax malaria, male sex, and adults.
This study suggests that areas with varying levels of transmission-intensities exhibited distinct characteristics. Malaria was distributed in a markedly disproportionate manner throughout the region, emphasising the need for spatially targeted interventions. Periodic quantification and characterisation of risk heterogeneity at various spatial levels using routine malaria surveillance data may aid in tracking progress towards elimination and guiding evidence-informed prioritisation of resource allocation.
The study was funded by the Australian Government Department of Foreign Affairs and Trade Indo-Pacific Centre for Health Security through the Strengthening Preparedness in the Asia-Pacific Region through Knowledge (SPARK) project.
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