Examining the influence of community leaders and other community actors on immunisation practices in Australia: A national cross-sectional study.

BACKGROUND: Variation in COVID-19 vaccination coverage and increasing vaccine hesitancy are well documented, especially amongst ethnic minority populations and current channels of vaccine and communication have been found to be inadequate. It has been suggested that more be done to utilise community-led pathways to improve vaccine readiness in ethnic minority communities in Australia. The study aimed to explore receptiveness towards the role of different actors and methods of communication about immunisation. METHODS: A cross-sectional survey of 1,227 adults in Australia was conducted to examine the roles of various actors in promoting vaccine uptake. Chi-square analyses and independent samples t-tests were used to identify significant associations between sociodemographic characteristics, vaccine practices, and vaccine information-seeking behaviours and (1) COVID-19 vaccine uptake (at least one dose) and (2) speaking a language other than English. RESULTS: At the time of the survey, 93% of respondents had received at least one dose of the COVID-19 vaccine. There were significant associations between COVID-19 vaccine uptake and: perceived capacity to locate accurate and timely vaccine information; receiving the COVID-19 vaccination information from a Nurse or Pharmacist; and receiving a vaccine recommendation by a GP. Additionally, respondents who spoke a language other than English reported were significantly more likely to have received information from family, friends, workplaces, local councils, religious centres, community leaders, and religious leaders than those who only spoke English. CONCLUSION: Significant variations in vaccine practices and vaccine information-seeking behaviours were found, especially in those who speak a language other than English. To enhance vaccine uptake and to address vaccine hesitancy in Australia, vaccine promotion strategies and health communication efforts require significant consideration of information accessibility and communication source preferences.

Oral docetaxel plus encequidar - A pharmacokinetic model and evaluation against IV docetaxel.

The development of optimized dosing regimens plays a crucial role in oncology drug development. This study focused on the population pharmacokinetic modelling and simulation of docetaxel, comparing the pharmacokinetic exposure of oral docetaxel plus encequidar (oDox + E) with the standard of care intravenous (IV) docetaxel regimen. The aim was to evaluate the feasibility of oDox + E as a potential alternative to IV docetaxel. The article demonstrates an approach which aligns with the FDA's Project Optimus which aims to improve oncology drug development through model informed drug development (MIDD). The key question answered by this study was whether a feasible regimen of oDox + E existed. The purpose of this question was to provide an early GO / NO-GO decision point to guide drug development and improve development efficiency. METHODS:  A stepwise approach was employed to develop a population pharmacokinetic model for total and unbound docetaxel plasma concentrations after IV docetaxel and oDox + E administration. Simulations were performed from the final model to assess the probability of target attainment (PTA) for different oDox + E dose regimens (including multiple dose regimens) in relation to IV docetaxel using AUC over effective concentration (AUCOEC) metric across a range of effective concentrations (EC). A Go / No-Go framework was defined-the first part of the framework assessed whether a feasible oDox + E regimen existed (i.e., a PTA ≥ 80%), and the second part defined the conditions to proceed with a Go decision. RESULTS:  The overall population pharmacokinetic model consisted of a 3-compartment model with linear elimination, constant bioavailability, constant binding mechanics, and a combined error model. Simulations revealed that single dose oDox + E regimens did not achieve a PTA greater than 80%. However, two- and three-dose regimens at 600 mg achieved PTAs exceeding 80% for certain EC levels. CONCLUSION:  The study demonstrates the benefits of MIDD using oDox + E as a motivating example. A population pharmacokinetic model was developed for the total and unbound concentration in plasma of docetaxel after administration of IV docetaxel and oDox + E. The model was used to simulate oDox + E dose regimens which were compared to the current standard of care IV docetaxel regimen. A GO / NO-GO framework was applied to determine whether oDox + E should progress to the next phase of drug development and whether any conditions should apply. A two or three-dose regimen of oDox + E at 600 mg was able to achieve non-inferior pharmacokinetic exposure to current standard of care IV docetaxel in simulations. A Conditional GO decision was made based on this result and further quantification of the "effective concentration" would improve the ability to optimise the dose regimen.

Use of Novel National Data Sets to Monitor Chemotherapy Use and Outcomes: A Retrospective Cohort Study of Non-Small-Cell Lung Cancer in Aotearoa New Zealand.

PURPOSE: Te Aho o Te Kahu, the New Zealand Cancer Control Agency, is establishing a systemic anticancer therapy (SACT) database (Anti-Cancer Therapy-Nationally Organized Workstream [ACT-NOW]) which can be linked to other national health data collections. In this article, we explore the application of ACT-NOW data in the monitoring of uptake and outcomes after the public funding of pemetrexed in Aotearoa New Zealand. METHODS: We used the ACT-NOW collection to identify patients with advanced nonsquamous non-small-cell lung cancer, who were treated with first-line platinum-based doublet chemotherapy over an 8-year period. Data were extracted for a period of 4 years before and 4 years after the national funding of pemetrexed (November 1, 2017). Treatments were classified as historical platinum doublet (cisplatin or carboplatin with gemcitabine, vinorelbine, paclitaxel, or docetaxel) or platinum pemetrexed doublet (cisplatin or carboplatin with pemetrexed). The primary outcome was the proportion of patients receiving each treatment type, before and after November 1, 2017. To prototype linkage to outcomes data, we evaluated hospitalization and 1-year overall survival (OS) rates by treatment. RESULTS: A total of 331 patients were included from four cancer centers. All patients (116 of 116) who were treated with first-line platinum-based doublet chemotherapy between November 2013 and November 2017 received historical platinum doublet chemotherapy. After the introduction of pemetrexed, between November 2017 and November 2021, 94% (203 of 215) were treated with platinum pemetrexed doublet chemotherapy and 6% (12 of 215) with historical platinum doublet chemotherapy. Linkage to outcomes data for 1-year OS, hospitalization rates, and lengths of stay outcome data were achievable. CONCLUSION: The ACT-NOW data set has the potential to facilitate evaluation of the impact of national-level SACT funding decisions on prescribing practice and specific patient outcomes. Our results support the use of these data to inform resource planning and quality improvement.

Optimal sample selection applied to information rich, dense data.

Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.

The effects of leadership levels and gender on leader well-being.

Previous research examining differences in levels of well-being between leaders and nonleaders has yielded mixed results. To explain the inconsistencies, we compare levels of well-being among nonleaders, mid-level leaders, and high-level leaders. Drawing from the job demands-resources model (Bakker & Demerouti, 2007, 2017) and the expanded version proposed by Crawford et al. (2010), we anticipate mid-level leaders will have lower levels of well-being compared to senior leaders and nonleaders, and females will be more vulnerable than males in mid-level leadership. In Study 1, we use multilevel models and propensity score matching (N = 24,067) and find mid-level leaders have worse general health conditions compared to nonleaders and high-level leaders, and that this effect is more pronounced among females. In Study 2, we collect experience sampling data from workers (N = 86; 1,634 observations) who completed a short survey four times daily for five consecutive working days. Mid-level leaders report more end-of-day negative emotions than high-level leaders and nonleaders, mediated by higher job demands and lower levels of job control to combat the negative effects of job demands; an effect that is particularly pronounced among female respondents. In Study 3, we use a two-wave time-lagged survey study (N = 330) and find middle managers have more challenge and hindrance job demands than nonleaders, and insufficient job control to offset the negative effects of job demands. Female middle managers report the most physical health symptoms and enjoy less eudaemonic well-being than their male counterparts. We conclude that leadership levels and gender have important and overlooked impacts on well-being. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

hybpiper-nf and paragone-nf: Containerization and additional options for target capture assembly and paralog resolution.

Premise: The HybPiper pipeline has become one of the most widely used tools for the assembly of target capture data for phylogenomic analysis. After the production of locus sequences and before phylogenetic analysis, the identification of paralogs is a critical step for ensuring the accurate inference of evolutionary relationships. Algorithmic approaches using gene tree topologies for the inference of ortholog groups are computationally efficient and broadly applicable to non-model organisms, especially in the absence of a known species tree. Methods and Results: We containerized and expanded the functionality of both HybPiper and a pipeline for the inference of ortholog groups, providing novel options for the treatment of target capture sequence data, and allowing seamless use of the outputs of the former as inputs for the latter. The Singularity container presented here includes all dependencies, and the corresponding pipelines (hybpiper-nf and paragone-nf, respectively) are implemented via two Nextflow scripts for easier deployment and to vastly reduce the number of commands required for their use. Conclusions: The hybpiper-nf and paragone-nf pipelines are easily installed and provide a user-friendly experience and robust results to the phylogenetic community. They are used by the Australian Angiosperm Tree of Life project. The pipelines are available at https://github.com/chrisjackson-pellicle/hybpiper-nf and https://github.com/chrisjackson-pellicle/paragone-nf.

Emergency presentation prior to lung cancer diagnosis: A national-level examination of disparities and survival outcomes.

OBJECTIVES: A recent multinational investigation of emergency presentation within 30 days of cancer diagnosis, conducted within the International Cancer Benchmarking Programme (ICBP), observed that New Zealand had the highest rate of emergency presentation prior to lung cancer diagnosis compared to other similar countries. Here we use national-level health data to further investigate these trends, focussing on ethnic disparities in emergency presentation prior to lung cancer diagnosis. We have also compared survival outcomes between those who had an emergency presentation in the preceding 30 days to those who did not. MATERIALS AND METHODS: Our study included all lung cancer registrations between 2007 and 2019 on the New Zealand Cancer Registry (N = 27,869), linked to national hospitalisation and primary healthcare data. We used descriptive (crude and age-standardised proportions) and logistic regression (crude and adjusted odds ratios) analyses to examine primary care access prior to cancer diagnosis, emergency hospitalisation up to and including 30 days prior to diagnosis, and one-year mortality post-diagnosis, both for the total population and between ethnicities. Regression models adjusted for age, sex, deprivation, rurality, comorbidity, tumour type and stage. RESULTS: We found stark disparities by ethnic group, with 62% of Pacific peoples and 54% of Maori having an emergency presentation within 30 days prior to diagnosis, compared to 47% of Europeans. These disparities remained after adjusting for multiple covariates including comorbidity and deprivation (adj. OR: Maori 1.21, 95% CI 1.13-1.30; Pacific 1.50, 95% CI 1.31-1.71). Emergency presentation was associated with substantially poorer survival outcomes across ethnic groups (e.g. 1-year mortality for Maori: no emergency presentation 50%, emergency presentation 79%; adj. OR 2.40, 95% CI 2.10-2.74). CONCLUSIONS: These observations reinforce the need for improvements in the early detection of lung cancer, particularly for Maori and Pacific populations, with a view to preventing diagnosis of these cancers in an emergency setting.

A genome resource for Acacia, Australia's largest plant genus.

Acacia (Leguminosae, Caesalpinioideae, mimosoid clade) is the largest and most widespread genus of plants in the Australian flora, occupying and dominating a diverse range of environments, with an equally diverse range of forms. For a genus of its size and importance, Acacia currently has surprisingly few genomic resources. Acacia pycnantha, the golden wattle, is a woody shrub or tree occurring in south-eastern Australia and is the country's floral emblem. To assemble a genome for A. pycnantha, we generated long-read sequences using Oxford Nanopore Technology, 10x Genomics Chromium linked reads, and short-read Illumina sequences, and produced an assembly spanning 814 Mb, with a scaffold N50 of 2.8 Mb, and 98.3% of complete Embryophyta BUSCOs. Genome annotation predicted 47,624 protein-coding genes, with 62.3% of the genome predicted to comprise transposable elements. Evolutionary analyses indicated a shared genome duplication event in the Caesalpinioideae, and conflict in the relationships between Cercis (subfamily Cercidoideae) and subfamilies Caesalpinioideae and Papilionoideae (pea-flowered legumes). Comparative genomics identified a suite of expanded and contracted gene families in A. pycnantha, and these were annotated with both GO terms and KEGG functional categories. One expanded gene family of particular interest is involved in flowering time and may be associated with the characteristic synchronous flowering of Acacia. This genome assembly and annotation will be a valuable resource for all studies involving Acacia, including the evolution, conservation, breeding, invasiveness, and physiology of the genus, and for comparative studies of legumes.

European-Australasian consensus on the management of advanced gastric and gastro-oesophageal junction cancer: current practice and new directions.

Gastric carcinoma and gastro-oesophageal junction (GC/GEJ) carcinoma remain a significant global problem, with patients presenting with symptoms often found to have advanced or metastatic disease. Treatment options for these patients have broadened in recent years with new chemotherapy agents, agents targeting angiogenic pathways and the development of immune checkpoint inhibitors (ICIs). Most initial advances have occurred in the refractory setting, where it is important to balance treatment benefits versus toxicity and patient quality of life. In the first-line treatment of advanced/metastatic GC/GEJ, platinum- and fluoropyrimidine-based chemotherapy protocols remain the backbone of therapy (with or without HER2-targeted therapy), with the FOLFIRI regimen offering an alternative in patients deemed unsuitable for a platinum agent. Microsatellite instability-high or mismatch repair-deficient cancers have been shown to benefit most from ICIs. In unselected patients previously treated with doublet or triplet platinum- and fluoropyrimidine-based chemotherapy and second-line chemotherapy with irinotecan or taxanes have formed the backbone of therapy with or without the addition of the vascular endothelial growth factor receptor-2 inhibitor ramucirumab in addition to paclitaxel. Beyond this, efficacy has been demonstrated with oral trifluridine/tipiracil and with single-agent nivolumab, in selected refractory patients. In this review, we highlight the positive evidence from key trials that have led to our current practice algorithm, with particular focus on the refractory advanced disease setting, discussing the areas of active research and highlighting the factors, including biomarkers and the influence of ethnicity, that contribute to therapeutic decision-making.

Personal Beliefs about Illness Questionnaire-Revised (PBIQ-R): Spanish adaptation in a clinical sample with psychotic disorders.

BACKGROUND: The way people with psychosis psychologically adapt and manage the diagnosis of such a mental disorder has been considered a key factor that contributes to the emergence and aggravation of emotional problems. These beliefs about illness can be very important due to their possible association with stigma and its implications in terms of loss of roles and social status. Given the importance of these personal beliefs about the specific diagnosis of psychosis, the Personal Beliefs about Illness Questionnaire (PBIQ) and PBIQ-R have been developed. AIMS: The present study aims to explore the psychometric characteristics of the Spanish version of the PBIQ-R in a sample of patients with a diagnosis of psychosis-related disorders. METHOD: Participants were 155 patients (54.8% male) of the Public Health Service in Andalusia (Spain). Those who consented to participate filled in the PBIQ-R, the Social Comparison Scale, and the PHQ-9 and GAD-7 to measure emotional symptoms. RESULTS: All dimensions showed adequate internal consistency values: Cronbach's alpha extends between .81 and .88; and McDonald's omega ranges between .87 and .92. The temporal reliability for an interval of 3-4 weeks was high. The correlations between the PBIQ-R dimensions and the other variables included in the study were significant and in the expected direction. The factor analysis of the principal components of the PBIQ-R dimensions revealed a single factor in each of the dimensions that explained 64-74%. CONCLUSIONS: The results support the reliability and validity of the Spanish version of the PBIQ-R.

Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model.

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements maybe evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomized two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritizing participants at an imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomization (RAR), discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.

Reference Case Methods for Expert Elicitation in Health Care Decision Making.

BACKGROUND: The evidence used to inform health care decision making (HCDM) is typically uncertain. In these situations, the experience of experts is essential to help decision makers reach a decision. Structured expert elicitation (referred to as elicitation) is a quantitative process to capture experts' beliefs. There is heterogeneity in the existing elicitation methodology used in HCDM, and it is not clear if existing guidelines are appropriate for use in this context. In this article, we seek to establish reference case methods for elicitation to inform HCDM. METHODS: We collated the methods available for elicitation using reviews and critique. In addition, we conducted controlled experiments to test the accuracy of alternative methods. We determined the suitability of the methods choices for use in HCDM according to a predefined set of principles for elicitation in HCDM, which we have also generated. We determined reference case methods for elicitation in HCDM for health technology assessment (HTA). RESULTS: In almost all methods choices available for elicitation, we found a lack of empirical evidence supporting recommendations. Despite this, it is possible to define reference case methods for HTA. The reference methods include a focus on gathering experts with substantive knowledge of the quantities being elicited as opposed to those trained in probability and statistics, eliciting quantities that the expert might observe directly, and individual elicitation of beliefs, rather than solely consensus methods. It is likely that there are additional considerations for decision makers in health care outside of HTA. CONCLUSIONS: The reference case developed here allows the use of different methods, depending on the decision-making setting. Further applied examples of elicitation methods would be useful. Experimental evidence comparing methods should be generated.

Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this article, we explore the potential of quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) estimation to reduce the computational cost of estimating EVPPI by reducing the variance compared with MC while preserving accuracy. We also develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using 2 examples: a simplified decision tree model for treatments for depression and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of generalized additive model (GAM) regression, Gaussian process (GP) regression, and integrated nested Laplace approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated and when the EVPPI is large. We also found that GP and INLA-GP were biased in those situations, whereas GAM cannot estimate EVPPI for large parameter sets.

Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context.

BACKGROUND: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. METHODS: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date. RESULTS: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June. CONCLUSIONS: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.

New targets acquired: Improving locus recovery from the Angiosperms353 probe set.

PREMISE: Universal target enrichment kits maximize utility across wide evolutionary breadth while minimizing the number of baits required to create a cost-efficient kit. The Angiosperms353 kit has been successfully used to capture loci throughout the angiosperms, but the default target reference file includes sequence information from only 6-18 taxa per locus. Consequently, reads sequenced from on-target DNA molecules may fail to map to references, resulting in fewer on-target reads for assembly, and reducing locus recovery. METHODS: We expanded the Angiosperms353 target file, incorporating sequences from 566 transcriptomes to produce a 'mega353' target file, with each locus represented by 17-373 taxa. This mega353 file is a drop-in replacement for the original Angiosperms353 file in HybPiper analyses. We provide tools to subsample the file based on user-selected taxon groups, and to incorporate other transcriptome or protein-coding gene data sets. RESULTS: Compared to the default Angiosperms353 file, the mega353 file increased the percentage of on-target reads by an average of 32%, increased locus recovery at 75% length by 49%, and increased the total length of the concatenated loci by 29%. DISCUSSION: Increasing the phylogenetic density of the target reference file results in improved recovery of target capture loci. The mega353 file and associated scripts are available at: https://github.com/chrisjackson-pellicle/NewTargets.

Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, presents a global public health challenge. Hospitals have been at the forefront of this battle, treating large numbers of sick patients over several waves of infection. Finding ways to manage the spread of the virus in hospitals is key to protecting vulnerable patients and workers, while keeping hospitals running, but to generate effective infection control, researchers must understand how SARS-CoV-2 spreads. A range of factors make studying the transmission of SARS-CoV-2 in hospitals tricky. For instance, some people do not present any symptoms, and, amongst those who do, it can be difficult to determine whether they caught the virus in the hospital or somewhere else. However, comparing the genetic information of the SARS-CoV-2 virus from different people in a hospital could allow scientists to understand how it spreads. Samples of the genetic material of SARS-CoV-2 can be obtained by swabbing infected individuals. If the genetic sequences of two samples are very different, it is unlikely that the individuals who provided the samples transmitted the virus to one another. Illingworth, Hamilton et al. used this information, along with other data about how SARS-CoV-2 is transmitted, to develop an algorithm that can determine how the virus spreads from person to person in different hospital wards. To build their algorithm, Illingworth, Hamilton et al. collected SARS-CoV-2 genetic data from patients and staff in a hospital, and combined it with information about how SARS-CoV-2 spreads and how these people moved in the hospital . The algorithm showed that, for the most part, patients were infected by other patients (20 out of 22 cases), while staff were infected equally by patients and staff. By further probing these data, Illingworth, Hamilton et al. revealed that 80% of hospital-acquired infections were caused by a group of just 21% of individuals in the study, identifying a 'superspreader' pattern. These findings may help to inform SARS-CoV-2 infection control measures to reduce spread within hospitals, and could potentially be used to improve infection control in other contexts.

A Review of Web-Based Tools for Value-of-Information Analysis.

Value-of-information analysis (VOI) is a decision-theoretic approach that is used to inform reimbursement decisions, optimise trial design and set research priorities. The application of VOI analysis for informing policy decisions in practice has been limited due, in part, to the perceived complexity associated with the calculation of VOI measures. Recent efforts have resulted in the development of efficient methods to estimate VOI measures and the development of user-friendly web-based tools to facilitate VOI calculations. We review the existing web-based tools including Sheffield Accelerated Value of Information (SAVI), the web interface to the BCEA (Bayesian Cost-Effectiveness Analysis) R package (BCEAweb), Rapid Assessment of Need for Evidence (RANE), and Value of Information for Cardiovascular Trials and Other Comparative Research (VICTOR). We describe what each tool is designed to do, the inputs they require, and the outputs they produce. Finally, we discuss how tools for VOI calculations might be improved in the future to facilitate the use of VOI analysis in practice.