Historical plant introductions predict current insect invasions.

Thousands of insect species have been introduced outside of their native ranges, and some of them strongly impact ecosystems and human societies. Because a large fraction of insects feed on or are associated with plants, nonnative plants provide habitat and resources for invading insects, thereby facilitating their establishment. Furthermore, plant imports represent one of the main pathways for accidental nonnative insect introductions. Here, we tested the hypothesis that plant invasions precede and promote insect invasions. We found that geographical variation in current nonnative insect flows was best explained by nonnative plant flows dating back to 1900 rather than by more recent plant flows. Interestingly, nonnative plant flows were a better predictor of insect invasions than potentially confounding socioeconomic variables. Based on the observed time lag between plant and insect invasions, we estimated that the global insect invasion debt consists of 3,442 region-level introductions, representing a potential increase of 35% of insect invasions. This debt was most important in the Afrotropics, the Neotropics, and Indomalaya, where we expect a 10 to 20-fold increase in discoveries of new nonnative insect species. Overall, our results highlight the strong link between plant and insect invasions and show that limiting the spread of nonnative plants might be key to preventing future invasions of both plants and insects.

Determining sample size in a personalized randomized controlled (PRACTical) trial.

In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers.

Sharing information across patient subgroups to draw conclusions from sparse treatment networks.

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large-sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.

The Personalised Randomized Controlled Trial: Evaluation of a new trial design.

In some clinical scenarios, for example, severe sepsis caused by extensively drug resistant bacteria, there is uncertainty between many common treatments, but a conventional multiarm randomized trial is not possible because individual participants may not be eligible to receive certain treatments. The Personalised Randomized Controlled Trial design allows each participant to be randomized between a "personalised randomization list" of treatments that are suitable for them. The primary aim is to produce treatment rankings that can guide choice of treatment, rather than focusing on the estimates of relative treatment effects. Here we use simulation to assess several novel analysis approaches for this innovative trial design. One of the approaches is like a network meta-analysis, where participants with the same personalised randomization list are like a trial, and both direct and indirect evidence are used. We evaluate this proposed analysis and compare it with analyses making less use of indirect evidence. We also propose new performance measures including the expected improvement in outcome if the trial's rankings are used to inform future treatment rather than random choice. We conclude that analysis of a personalized randomized controlled trial can be performed by pooling data from different types of participants and is robust to moderate subgroup-by-intervention interactions based on the parameters of our simulation. The proposed approach performs well with respect to estimation bias and coverage. It provides an overall treatment ranking list with reasonable precision, and is likely to improve outcome on average if used to determine intervention policies and guide individual clinical decisions.

Practical approaches to Bayesian sample size determination in non-inferiority trials with binary outcomes.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone.

A new approach to evaluating loop inconsistency in network meta-analysis.

In network meta-analysis, studies evaluating multiple treatment comparisons are modeled simultaneously, and estimation is informed by a combination of direct and indirect evidence. Network meta-analysis relies on an assumption of consistency, meaning that direct and indirect evidence should agree for each treatment comparison. Here we propose new local and global tests for inconsistency and demonstrate their application to three example networks. Because inconsistency is a property of a loop of treatments in the network meta-analysis, we locate the local test in a loop. We define a model with one inconsistency parameter that can be interpreted as loop inconsistency. The model builds on the existing ideas of node-splitting and side-splitting in network meta-analysis. To provide a global test for inconsistency, we extend the model across multiple independent loops with one degree of freedom per loop. We develop a new algorithm for identifying independent loops within a network meta-analysis. Our proposed models handle treatments symmetrically, locate inconsistency in loops rather than in nodes or treatment comparisons, and are invariant to choice of reference treatment, making the results less dependent on model parameterization. For testing global inconsistency in network meta-analysis, our global model uses fewer degrees of freedom than the existing design-by-treatment interaction approach and has the potential to increase power. To illustrate our methods, we fit the models to three network meta-analyses varying in size and complexity. Local and global tests for inconsistency are performed and we demonstrate that the global model is invariant to choice of independent loops.

Alien insect dispersal mediated by the global movement of commodities.

Globalization and economic growth are recognized as key drivers of biological invasions. Alien species have become a feature of almost every biological community worldwide, and rates of new introductions continue to rise as the movement of people and goods accelerates. Insects are among the most numerous and problematic alien organisms, and are mainly introduced unintentionally with imported cargo or arriving passengers. However, the processes occurring prior to insect introductions remain poorly understood. We used a unique dataset of 1,902,392 border interception records from inspections at air, land, and maritime ports in Australia, New Zealand, Europe, Japan, USA, and Canada to identify key commodities associated with insect movement through trade and travel. In total, 8939 species were intercepted, and commodity association data were available for 1242 species recorded between 1960 and 2019. We used rarefaction and extrapolation methods to estimate the total species richness and diversity associated with different commodity types. Plant and wood products were the main commodities associated with insect movement across cargo, passenger baggage, and international mail. Furthermore, certain species were mainly associated with specific commodities within these, and other broad categories. More closely related species tended to share similar commodity associations, but this occurred largely at the genus level rather than within orders or families. These similarities within genera can potentially inform pathway management of new alien species. Combining interception records across regions provides a unique window into the unintentional movement of insects, and provides valuable information on establishment risks associated with different commodity types and pathways.

Experiences of implementing the 'Making Every Contact Count' initiative into a UK integrated care system: an interview study.

BACKGROUND: The 'Making Every Contact Count' (MECC) approach is in line with the current National Health Service (NHS) strategy to improve and prevent health conditions in England. Despite its importance and value for preventative healthcare, implementation of MECC varies. The aim of this study was to explore the barriers and facilitators of implementing MECC and MECC training into an integrated care system (ICS). METHODS: Remote semi-structured interviews were conducted with staff across an ICS in the North West of England who were involved in implementing and delivering MECC across the region. Data were analysed initially using an inductive thematic analysis approach and then interpreted using the 'Capability, Opportunity, Motivation = Behaviour' (COM-B) model of behaviour change. RESULTS: We interviewed nine stakeholders and identified three superordinate themes: (1) macro-level barriers and facilitators, e.g. funding; (2) organizational level barriers and facilitators, e.g. time and resource; and (3) individual-level barriers/facilitators for both MECC trainers and MECC agents. CONCLUSIONS: MECC has potential to meet the needs of the public's health, but barriers to its implementation exist. MECC must be successfully embedded into organizations and regions in which it is implemented, which relies on further development of an appropriate infrastructure including sustainable funding and a shift in culture to value preventative healthcare.

A qualitative interview study applying the COM-B model to explore how hospital-based trainers implement antimicrobial stewardship education and training in UK hospital-based care.

BACKGROUND: Antimicrobial resistance (AMR) is a major global health threat caused by the inappropriate use of antimicrobials in healthcare and other settings. Antimicrobial stewardship (AMS) is a broad multi-component health services intervention that promotes and monitors the judicious use of antimicrobials to preserve their future effectiveness. A main component of AMS is education and training (E&T). However, there are often discrepancies in how such interventions are implemented and delivered in hospital-based care. The aim of this study was to explore the factors influencing the implementation of AMS E&T in UK hospitals. METHODS: Semi-structured interviews were carried out with AMS E&T trainers in UK hospitals. The interview schedule was developed using the Capability, Opportunity, Motivation = Behaviour (COM-B) model. Participants were identified via professional networks and social media. Interviews were analysed using inductive thematic analysis, followed by deductive analysis using the COM-B model as a framework. RESULTS: A total of 34 participants (26 antimicrobial pharmacists, 3 nurses, 1 advanced clinical practitioner, 2 infectious disease consultants, 1 microbiologist and 1 clinical scientist). responsible for designing, implementing and evaluating AMS E&T in UK hospitals (five from Northern Ireland, four from Wales, two from Scotland and 23 from England) took part in virtual interviews. Key themes were: (1) The organisational context, including system-level barriers to AMS included competing organisational targets (Reflective motivation and physical opportunity) and the impact of the COVID-19 pandemic on activity (Physical opportunity); (2) Healthcare professionals' roles and the wider multi-disciplinary team, such that AMS roles were defined and addressed poorly in E&T (Social opportunity); and (3) The individual perception of the need for AMS E&T in hospital-based care, manifest in a perceived lack of conviction of the wider threat of AMR and the resulting need for AMS E&T (Reflective motivation). CONCLUSION: This study has identified factors influencing implementation of AMS E&T in UK hospitals and further identified where implemented, AMS E&T did not address real-world challenges. Current AMS E&T needs to be optimised to elicit practice change, with recommendations including training and engaging the wider work-force and drawing upon theoretically-informed intervention development frameworks to inform AMS E&T to better target AMS behaviour change.

Automated Video-Based Capture of Crustacean Fisheries Data Using Low-Power Hardware.

This work investigates the application of Computer Vision to the problem of the automated counting and measuring of crabs and lobsters onboard fishing boats. The aim is to provide catch count and measurement data for these key commercial crustacean species. This can provide vital input data for stock assessment models, to enable the sustainable management of these species. The hardware system is required to be low-cost, have low-power usage, be waterproof, available (given current chip shortages), and able to avoid over-heating. The selected hardware is based on a Raspberry Pi 3A+ contained in a custom waterproof housing. This hardware places challenging limitations on the options for processing the incoming video, with many popular deep learning frameworks (even light-weight versions) unable to load or run given the limited computational resources. The problem can be broken into several steps: (1) Identifying the portions of the video that contain each individual animal; (2) Selecting a set of representative frames for each animal, e.g, lobsters must be viewed from the top and underside; (3) Detecting the animal within the frame so that the image can be cropped to the region of interest; (4) Detecting keypoints on each animal; and (5) Inferring measurements from the keypoint data. In this work, we develop a pipeline that addresses these steps, including a key novel solution to frame selection in video streams that uses classification, temporal segmentation, smoothing techniques and frame quality estimation. The developed pipeline is able to operate on the target low-power hardware and the experiments show that, given sufficient training data, reasonable performance is achieved.

"I was simply trying to make it through the day": A collaborative autoethnography of couple/marriage and family therapy program directors in a diversity and anti-racism peer consultation group.

The COVID-19 pandemic and widely depicted incidents of racial injustice in the United States caused marked stress and shifts in society in 2020, leading to an acceleration of discussions related to promoting diversity, equity, inclusion, and justice (DEIJ) in family-oriented mental health professions, including through training. Despite the consequential role leaders of academic programs play in overseeing didactic and clinical training, little research has examined approaches for supporting academic leaders in promoting DEIJ in family science-related academic training programs. In this collaborative autoethnography, we, six participants in a diversity and anti-racism peer consultation group for leaders of couple/marriage and family therapy (C/MFT) programs, present our experiences participating in the group over the past two years. At the start of the group, many of us were experiencing profound isolation and stress due to intensified responsibilities subsequent to the COVID-19 pandemic and broadcast depictions of racial injustice. We experienced the group as a safe, inclusive space to grow personally and professionally, which subsequently inspired us to make changes in our programs. We also recognized the need for greater infrastructure to support program directors in developing DEIJ leadership skills. Future directions for research include examining experiences and outcomes of director-initiated DEIJ change, as well examination of DEIJ-focused peer consultation groups among family systems-oriented academic leaders of diverse disciplines and nations.

Implications of analysing time-to-event outcomes as binary in meta-analysis: empirical evidence from the Cochrane Database of Systematic Reviews.

BACKGROUND: Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures. METHODS: We extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only ("binary"), or in binary form together with observed minus expected and variance statistics ("OEV"). We explored how results for time-to-event outcomes originally analysed as "binary" change when analysed using the complementary log-log (clog-log) link on a HR scale. For the data originally analysed as HRs ("OEV"), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale. RESULTS: The pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses. CONCLUSIONS: We identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log-log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses.

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial.

BACKGROUND: Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. METHODS: We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. RESULTS: The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis. CONCLUSIONS: Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

Evaluating dermatology education and training.

The purpose of education and training in dermatology ranges from increasing knowledge and skills to improving confidence or enhancing patient outcomes. Specification of the purpose of the education and training is vital so that evaluation can be aligned to purpose, and thus provide evidence on effectiveness. Further, the quality and quality improvement of education and training can be enhanced by a careful specification of how they are expected to achieve their purpose. Multiple theories and methods can be used to evaluate both the process and the outcome of education and training. In this paper, we summarize some of these and focus particularly on the use of behavioural science to evaluate education and training. We illustrate these theories and methods with an example in dermatology.

Development of a B-cell maturation antigen-specific T-cell antigen coupler receptor for multiple myeloma.

BACKGROUND AIMS: T cells engineered with synthetic receptors have delivered powerful therapeutic results for patients with relapsed/refractory hematologic malignancies. The authors have recently described the T-cell antigen coupler (TAC) receptor, which co-opts the endogenous T-cell receptor (TCR) and activates engineered T cells in an HLA-independent manner. Here the authors describe the evolution of a next-generation TAC receptor with a focus on developing a TAC-engineered T cell for multiple myeloma. METHODS: To optimize the TAC scaffold, the authors employed a bona fide antigen-binding domain derived from the B-cell maturation antigen-specific monoclonal antibody C11D5.3, which has been used successfully in the clinic. The authors first tested humanized versions of the UCHT1 domain, which is used by the TAC to co-opt the TCR. The authors further discovered that the signal peptide affected surface expression of the TAC receptor. Higher density of the TAC receptor enhanced target binding in vitro, which translated into higher levels of Lck at the immunological synapse and stronger proliferation when only receptor-ligand interactions were present. RESULTS: The authors observed that the humanized UCHT1 improved surface expression and in vivo efficacy. Using TAC T cells derived from both healthy donors and multiple myeloma patients, the authors determined that despite the influence of receptor density on early activation events and effector function, receptor density did not impact late effector functions in vitro, nor did the receptor density affect in vivo efficacy. CONCLUSIONS: The modifications to the TAC scaffold described herein represent an important step in the evolution of this technology, which tolerates a range of expression levels without impacting therapeutic efficacy.

Remote interventions to improve exercise behaviour in sedentary people living with and beyond cancer: a systematic review and meta-analysis.

BACKGROUND: The COVID-19 pandemic has forced many cancer services to consider a transition to a remote format of delivery that is largely untested. Accordingly, we sought to perform a systematic review of the effects of remotely delivered interventions to improve exercise behaviour in sedentary adults living with and beyond cancer. METHODS: Eligible studies were randomised controlled trials comparing a remotely delivered exercise intervention to a usual care comparison in sedentary people over 18 years old with a primary cancer diagnosis. Nine electronic databases were searched from inception to November 2020. RESULTS: The review included three trials, totalling 186 participants. Two of the included trials incorporated prescriptions that meet current aerobic exercise recommendations, one of which also meets the guidelines for resistance exercise. No trials reported an intervention adherence of 75% or more for a set prescription that meets current exercise guidelines. CONCLUSION: There is little evidence suggesting that remote exercise interventions promote exercise behaviours or improve physical function in sedentary adults living with and beyond cancer. The development and evaluation of novel remote exercise interventions is needed to establish their usefulness for clinical practice. Given the social response to the COVID-19 pandemic, further research in this area is urgently needed.

Worldwide border interceptions provide a window into human-mediated global insect movement.

As part of national biosecurity programs, cargo imports, passenger baggage, and international mail are inspected at ports of entry to verify compliance with phytosanitary regulations and to intercept potentially damaging nonnative species to prevent their introduction. Detection of organisms during inspections may also provide crucial information about the species composition and relative arrival rates in invasion pathways that can inform the implementation of other biosecurity practices such as quarantines and surveillance. In most regions, insects are the main taxonomic group encountered during inspections. We gathered insect interception data from nine world regions collected from 1995 to 2019 to compare the composition of species arriving at ports in these regions. Collectively, 8,716 insect species were intercepted in these regions over the last 25 yr, with the combined international data set comprising 1,899,573 interception events, of which 863,972 were identified to species level. Rarefaction analysis indicated that interceptions comprise only a small fraction of species present in invasion pathways. Despite differences in inspection methodologies, as well as differences in the composition of import source regions and imported commodities, we found strong positive correlations in species interception frequencies between regions, particularly within the Hemiptera and Thysanoptera. There were also significant differences in species frequencies among insects intercepted in different regions. Nevertheless, integrating interception data among multiple regions would be valuable for estimating invasion risks for insect species with high likelihoods of introduction as well as for identifying rare but potentially damaging species.

Towards implementing exercise into the prostate cancer care pathway: development of a theory and evidence-based intervention to train community-based exercise professionals to support change in patient exercise behaviour (The STAMINA trial).

BACKGROUND: The National Institute for Health and Care Excellence (NICE) recommend that men on androgen deprivation therapy (ADT) for prostate cancer should receive supervised exercise to manage the side-effects of treatment. However, these recommendations are rarely implemented into practice. Community-based exercise professionals (CBEPs) represent an important target group to deliver the recommendations nationally, yet their standard training does not address the core competencies required to work with clinical populations, highlighting a need for further professional training. This paper describes the development of a training package to support CBEPs to deliver NICE recommendations. METHODS: Development of the intervention was guided by the Medical Research Council guidance for complex interventions and the Behaviour Change Wheel. In step one, target behaviours, together with their barriers and facilitators were identified from a literature review and focus groups with CBEPs (n = 22) and men on androgen deprivation therapy (n = 26). Focus group outputs were mapped onto the Theoretical Domains Framework (TDF) to identify theoretical constructs for change. In step two, behaviour change techniques and their mode of delivery were selected based on psychological theories and evidence to inform intervention content. In step three, the intervention was refined following delivery and subsequent feedback from intervention recipients and stakeholders. RESULTS: Six modifiable CBEPs target behaviours were identified to support the delivery of the NICE recommendations. Nine domains of the TDF were identified as key determinants of change, including: improving knowledge and skills and changing beliefs about consequences. To target the domains, we included 20 BCTs across 8 training modules and took a blended learning approach to accommodate different learning styles and preferences. Following test delivery to 11 CBEPs and feedback from 28 stakeholders, the training package was refined. CONCLUSION: Established intervention development approaches provided a structured and transparent guide to intervention development. A training package for CBEPs was developed and should increase trust amongst patients and health care professionals when implementing exercise into prostate cancer care. Furthermore, if proven effective, the development and approach taken may provide a blueprint for replication in other clinical populations where exercise has proven efficacy but is insufficiently implemented.

The development of a theory and evidence-based intervention to aid implementation of exercise into the prostate cancer care pathway with a focus on healthcare professional behaviour, the STAMINA trial.

BACKGROUND: Twice-weekly supervised aerobic and resistance exercise for 12 weeks reduces fatigue and improves quality of life in men on Androgen Deprivation Therapy for prostate cancer. Despite the National Institute for Health and Care Excellence (NICE) proposing this as standard of care, it does not routinely take place in practice. Healthcare professionals are in a prime position to deliver and integrate these recommendations. A change in the behaviour of clinical teams is therefore required. In this paper, we describe the development of a training package for healthcare professionals using theory and evidence to promote delivery of such recommendations as standard care. METHODS: The intervention development process was guided by the Medical Research Council guidance for complex interventions and the Behaviour Change Wheel. Target behaviours were identified from the literature and thirty-five prostate cancer care healthcare professionals (including oncologists, consultant urologists, clinical nurse specialists, physiotherapists, general practitioners and commissioners) were interviewed to understand influences on these behaviours. The Theoretical Domains Framework was used to identify theoretical constructs for change. Behaviour change techniques were selected based on theory and evidence and were translated into intervention content. The intervention was refined with the input of stakeholders including healthcare professionals, patients, and exercise professionals in the form of rehearsal deliveries, focus groups and a workshop. RESULTS: Seven modifiable healthcare professional target behaviours were identified to support the delivery of the NICE recommendations including identifying eligible patients suitable for exercise, recommending exercise, providing information, exercise referral, providing support and interpret and feedback on progress. Ten domains from the Theoretical Domain's Framework were identified as necessary for change, including improving knowledge and skills, addressing beliefs about consequences, and targeting social influences. These were targeted through twenty-two behaviour change techniques delivered in a half-day, interactive training package. Based on initial feedback from stakeholders, the intervention was refined in preparation for evaluation. CONCLUSIONS: We designed an intervention based on theory, evidence, and stakeholder feedback to promote and support the delivery of NICE recommendations. Future work will aim to test this training package in a multi-centre randomised trial. If proven effective, the development and training package will provide a template for replication in other clinical populations, where exercise has proven efficacy but is insufficiently implemented.

Considering unseen arrivals in predictions of establishment risk based on border biosecurity interceptions.

Assessing species establishment risk is an important task used for informing biosecurity activities aimed at preventing biological invasions. Propagule pressure is a major contributor to the probability of invading species establishment; however, direct assessment of numbers of individuals arriving is virtually never possible. Inspections conducted at borders by biosecurity officials record counts of species (or higher-level taxa) intercepted during inspections, which can be used as proxies for arrival rates. Such data may therefore be useful for predicting species establishments, though some species may establish despite never being intercepted. We present a stochastic process-based model of the arrival-interception-establishment process to predict species establishment risk from interception count data. The model can be used to estimate the probability of establishment, both for species that were intercepted and species that had no interceptions during a given observation period. We fit the stochastic model to data on two insect families, Cerambycidae and Aphididae, that were intercepted and/or established in the United States or New Zealand. We also explore the effects of variation in model parameters and the inclusion of an Allee effect in the establishment probability. Although interception data sets contain much noise due to variation in inspection policy, interception effort and among-species differences in detectability, our study shows that it is possible to use such data for predicting establishments and distinguishing differences in establishment risk profile between taxonomic groups. Our model provides a method for predicting the number of species that have breached border biosecurity, including both species detected during inspections but also "unseen arrivals" that have never been intercepted, but have not yet established a viable population. These estimates could inform prioritization of different taxonomic groups, pathways or identification effort in biosecurity programs.