Patients reported pre‐ and postvisit if their cancer was at a late stage. Postvisit, patients reported if they discussed scan results with an oncologist or another oncology provider . Logistic regression analysis was used to determine if the presence of an oncologist during scan results discussions differentially predicted the patients' likelihood of postvisit late‐stage illness understanding (LSIU). Propensity weighting was used to correct for sociodemographic imbalances between groups, and previsit LSIU and the presence of multiple providers were controlled for in the logistic regression analyses. After propensity‐weighted adjustment and controlling for previsit LSIU and the presence of multiple providers, patients were 2.6 times more likely  to report that their disease was late stage if an oncologist was present for the scan results discussion compared to if an oncologist was absent. The presence of an oncologist during scan results discussions was associated with a higher likelihood of patients acknowledging being in a late stage of their disease. These results suggest that oncologist involvement in scan results discussions is associated with advanced cancer patients having better prognostic understanding.Realistic illness understanding is essential to an advanced cancer patient's ability to make informed medical decisions at the end of life. This study sought to determine whether advanced cancer patients better understood the late stage of their cancer if an oncologist, compared to other members of the care team, was present to discuss their scan results. Data were derived from a multi‐institutional, longitudinal cohort study of patients recruited between 2010 and 2015. Patients ( For patients to engage in informed end‐of‐life (EoL) decision‐making, they must have a realistic understanding of their prognosis what is said during discussions with advanced cancer patients about the status of their disease strongly influences whether patients understand their illness whom on the care team delivers this information to patients. Oncologists may be perceived by their patients to have the most advanced medical training  and to be the highest authority on the care team. The strong influence of authority on behavior is a well‐established psychological phenomenon Prior studies suggest that This study examines whether advanced cancer patients better understand the severity of their illness if a medical oncologist (hereafter oncologist) is present when scan results are discussed in clinic. We hypothesized that discussing scan results with an oncologist would be associated with greater late stage of disease acknowledgment.The patient sample analyzed in the present report was drawn from the Coping with Cancer II (CwC‐II) study, a National Cancer Institute‐funded, prospective, multi‐institutional cohort study of advanced cancer patients, their caregivers, and their oncology providers, designed to evaluate the relationship between EoL communication and EoL outcomes. Participants were recruited from one of eight medical centers across the United States, which were grouped into three geographical categories: (1) the Mid‐Atlantic/South which included the Meyer Cancer Center at Weill Cornell Medicine , Memorial Sloan Kettering Cancer Center , and Virginia Commonwealth University Massey Cancer Center ; (2) the Southwest/West which included Parkland Hospital , University of New Mexico Cancer Center , and Pomona Valley Hospital Medical Center ; and (3) New England which included Dana‐Farber/Harvard Cancer Center  and Yale Cancer Center .Criteria for patient eligibility included the following cancers: metastatic pancreaticobiliary, esophagogastric, hepatocellular, lung, or ovarian cancers that progressed on at least one chemotherapy regimen, or metastatic colorectal cancer that progressed on at least two chemotherapy regimens. Eligibility criteria also included age ≥21 years and the ability to complete study interviews. Patients who were cognitively impaired  or had received palliative care prior to enrollment were excluded. All study participants provided written informed consent. The institutional review boards of all participating institutions approved the study's conduct.Interviews were conducted between February 2010 and February 2015. Patients were interviewed at study entry (previsit) and then again within a week after an oncology visit in which recent scan results to evaluate potential disease progression were discussed (postvisit). These were not the first scan results that indicated patients had advanced cancer, as patients had to have previous scan results that showed progression on at least one chemotherapy regimen in order to be eligible for the study. The interviews were conducted by research assistants (RAs) who underwent extensive training on how to conduct the interviews in a sensitive and brief manner. The training was administered by an experienced, trained RA who practiced these techniques, and RAs were only permitted to interview subjects after demonstrating reliability, accuracy, and sensitivity. A total of 386 patients enrolled in the study Fig. . ImportaPatients provided information on their age, education, gender, race, ethnicity, health insurance status, and marital status. Patients' clinic site and primary cancer were also documented.r = 0.292, P < 0.001), whether the patient's cancer is curable , and whether the patient expects to live months or years .Patients' late‐stage illness understanding (LSIU) was assessed in the interviews at pre‐ and postvisit by asking: “What stage is your cancer?” Response options were: (1) no evidence of cancer, (2) early stage of cancer, (3) middle stage of cancer, (4) late stage of cancer, (5) end stage of cancer, and (6) don't know. Acknowledgment of late stage of cancer was coded “1” for response options 4 and 5, and “0” for response options 1, 2, 3, and 6. LSIU was shown to be a valid assessment of prognostic understanding and significantly and closely associated with other measures of this construct. Specifically, it was closely associated with questions asking if the patient was terminally ill (n = 5). This question was followed by: “If yes, with whom did you discuss the results of your scan?” The response options were as follows: (1) oncologist, (2) oncology fellow, (3) physician's assistant, (4) nurse practitioner, (5) nurse, (6) other, specify, and (7) oncology resident. Patients could select multiple response options. The presence of an oncologist was coded “1” for answers that included response option 1 and “0” for answers that included any response option(s) other than response option 1.During the postvisit interviews, patients were asked: “When you met with your oncology provider, did you discuss the results of your recent scan?” Patients who answered “no” were excluded . In the cases where this information was unavailable, scan results were determined based on the patient's report at postvisit of whether his or her oncology provider indicated whether his or her disease was progressing, stable, or improving based on his or her most recent scan . There was high reliability between what the providers and patients reported the scan results to be .Previsit LSIU, the presence of multiple providers during scan results discussions, and scan results were examined as potential confounding influences. Previsit LSIU was assessed using the measure outlined above for LSIU. The presence of multiple providers was evaluated based on whether participants selected multiple communicators of scan results when answering the question described above. Scan results were determined by whether the oncology provider who met with the patient to discuss his or her scan results reported to the interviewer that the patient's disease was progressing, stable, or improving based on the patient's most recent scan  associated with either the predictor (the presence of an oncologist) or the outcome (postvisit LSIU). We found that previsit LSIU was associated with the presence of an oncologist (P = 0.027) and postvisit LSIU (P < 0.001). The presence of multiple providers was associated with the presence of an oncologist (P < 0.001) but not postvisit LSIU (P = 0.907). Scan results were not associated with the presence of an oncologist (P = 0.151) or postvisit LSIU (P = 0.066). Consequently, previsit LSIU and the presence of multiple providers were included as covariates in the multivariable models, while scan results were not included.To determine whether nonsociodemographic characteristics  were potential confounders, we used chi‐square tests to identify whether the variables were significantly . Statistical inferences were based on two‐sided tests with Of the 209 patients in the analytic sample, 100 (47.8%) reported they discussed their scan results with an oncologist only, 45 (21.5%) reported they discussed their scan results with an oncologist and a nononcologist provider present, 62 (29.7%) reported they discussed their scan results with a nononcologist only, and 2 (1.0%) reported they discussed their scan results with more than one nononcologist. A total of 41.1% of patients acknowledged that their cancer was late stage during the previsit interview, and 47.8% of patients acknowledged that their cancer was late stage during the postvisit interview. Table P = 0.021; power = 0.90).Table Results of this study demonstrate that advanced cancer patients were over two‐and‐a‐half times as likely to describe their cancer as late stage if an oncologist was present for their scan results discussion in clinic compared to patients whose scan results were presented by a nononcologist member of the care team. This finding remained after propensity weighting corrected for sociodemographic differences between the patients who met with an oncologist to discuss scan results versus those who did not  and after adjusting for the confounding influences of previsit illness understanding and the presence of multiple providers during scan results discussions. These results suggest that it matters whom on the care team meets with patients in clinic to discuss scan results with advanced cancer patients and that oncologist engagement in these conversations is associated with more accurate patient illness understanding.Prior research has shown the influence of the content of EoL conversations on patient illness understanding There are several potential reasons why patients who discussed scan results with an oncologist were more likely to understand the late stage of their illness. First, oncologists would appear to hold the position of highest authority on the cancer care team, and therefore, their words may have the greatest impact. This is consistent with prior research on the powerful influence of authority on persuasion Differences in experience across care team members may also explain our findings. Research suggests that both oncology fellows and nurses  may lack sufficient training on how to communicate with patients and their families about EoL issues Another potential explanation for our results is that patients may expect to hear the “bad news”  from their oncologist. Thus, patients may treat scan results discussions with other members of the care team as the first part of the discussion about their disease progression and be unlikely to draw definitive conclusions about the stage of their cancer before speaking with their oncologist. Additionally, certain clinics may have rules about the extent to which nononcologists should discuss disease progression with patients, limiting the amount of information provided by nononcologists. Furthermore, oncologists may feel greater responsibility for shepherding the care of their patients than other members of the care team and so may more often explain the meaning or clinical implications of scan results  in addition to describing the results of the scan.It is notable that oncologists were absent from over 30% of scan results discussions, which highlights both how common this practice is and the need for attention to the consequences that may follow from it. It should be noted that comprehensive cancer centers would be expected to employ more oncologists than noncomprehensive cancer centers, and because comprehensive cancer centers were overrepresented in our sampling of patients, we believe the prevalence of nononcologist scan discussions to be a conservative estimate. At the Southwest/West clinics, oncologists were absent from about 52% of scan results discussions, which was a higher rate than at the Mid‐Atlantic/South and New England clinics. A possible explanation for this is that the Southwest/West region had more sites that were not comprehensive cancer centers, and oncologists may have been less readily available to meet with patients at these medical centers than at the other clinics. Future research should attempt to examine how cancer center type  influences scan results discussions. However, it is important to note that propensity weighting corrected the clinical differences in our analyses and our results held in the propensity‐weighted sample.Our study has several strengths, one of which is its unique design. It is a longitudinal study where advanced cancer patients were interviewed before and after scan results discussions with oncology providers. This allowed us to examine communication factors associated with changes in patients' understanding of their disease. The study also capitalizes on a prospective design in which selection biases that often confound retrospective studies Our study also had limitations that should be considered when interpreting the results. First, the “cancer stage” question used in our analyses represents only one element of patients' illness understanding. Still, patients' acknowledgment of late‐stage disease was highly correlated with other illness understanding indicators and was a key outcome in our recent report examining the impact of prognostic disclosure on prognostic understanding Additional research is needed to investigate what was said during the scan results discussions with providers. Examining the language used by providers will enable us to identify effective communication strategies, which could be incorporated into clinical training programs. Future research should also compare the impact of oncologists versus palliative care physicians on patient's illness understanding following EoL discussions. Like oncologists, palliative care physicians are senior, authoritative physicians who address illness understanding and EoL care issues with advanced cancer patients.In conclusion, this study found that advanced cancer patients better acknowledge that they have late‐stage disease when an oncologist is present for a scan results discussion in clinic. Our findings suggest it is important for oncologists to engage in scan results discussions directly with advanced cancer patients. Oncologists who are present in clinic to discuss scan results have patients who better understand the late stage of their disease and who are thus better equipped to make informed EoL care choices.None declared.Table S1. Characteristics of patients included in and excluded from the analytic sample.Click here for additional data file.


PACS number(s): 87.90.+y, 87.52.–g Dear Editor,et al. “Dosimetric advantage of using 6 MV over 15 MV photons in conformal therapy of lung cancer: Monte Carlo studies in patient geometries,” . In the abstract, the authors state that “In treatment planning of tumors that abut lung tissue, lower energy (6 MV) photon beams should be preferred over higher energies (15–18 MV) because of the significant loss of lateral dose equilibrium for high‐energy beams in the low‐density medium.” We would like to discuss this statement. In order to justify such a strong statement, one would expect (i) an accurate dose calculation algorithm to have been used for the treatment planning and plan evaluation, and (ii) a statistically significant number of cases to have been studied.We read with interest the recent article by Wang et al. perform the treatment planning using a pencil beam (PB) method and evaluate their plans using a Monte Carlo (MC) method. Because PB agrees more closely with MC at low energies, the plan evaluation using MC at high energies has poorer target coverage. If MC had been used for the treatment planning, the poor target coverage would have been observed, and then improved by setting field apertures that account for the increased electron range. Obviously, larger apertures mean that more of the lung is in the field, however, this is the necessary depth dose versus lateral spread trade‐off as the beam energy changes. Planning and evaluating the plans with the same (accurate) method at least ensures a fair comparison.(i) In their paper, Wang et al. were based on the treatment plans of two patients. With this sample size, it is feasible to hypothesize that lower energies are better than higher energies and perhaps state that a larger study would be useful. However, it is improper to conclude that lower energies are superior without proper statistical analysis. Drawing a general conclusion based on only the analysis of two patients is speculative .(ii) The conclusions of Wang Our experience using IMRT to compare low‐ and high‐energy beams for lung cancer radiotherapy conflicts with the statement above. In our work, lung IMRT plans were calculated at both 6 and 18 MV using an algorithm (superposition) that accounts for the increased electron range in low‐density media. The IMRT objective function score was better for the 18 MV plan than the 6 MV plan. The 18 MV plan was used for treatment.et al. have shown in their paper are discrepancies between treatment plans calculated by an MC method and those calculated by a PB algorithm. These discrepancies are larger for higher energy beams. Perhaps the statement above could be changed to: In treatment planning of tumors that abut lung tissue, in the absence of a dose calculation algorithm that fully accounts for the lateral electron range in low‐density media, lower energy (6 MV) photon beams should be preferred over higher energies (15–18 MV) to ensure target coverage.In summary, what Wang


Since Francis Peyton Rous’s landmark study of identifying avian oncogenic virus, seven viruses have been found to cause 10–15 % of human cancers worldwide  – might n = 91) and blood (n = 160) from Chinese women with breast cancer from four tissue banks in Anhui, Jiangsu, and Shanghai, in blood from women without breast cancer in Anhui (n = 100), and in blood from cows in Anhui, Beijing, Heilongjiang, and Jiangsu (n = 150).In this study, we conducted seven PCRs according to Buehring’s group , 4 and oPCR analysis revealed that BLV was positive in 50.0 % of the cattle blood samples but was undetectable in the blood from women with or without breast cancer. ELISA revealed that 47.3 % of the cattle blood samples were BLV antibody positive, whereas none of the blood samples from women with or without breast cancer were BLV antibody positive. Although eight published PCRs in our study could readily detect BLV in the blood samples of herds with a high percentage of positivity, these PCRs did not show positive for BLV in the samples of either breast tumor tissues or blood from Chinese women with breast cancer. Similarly, the commercial ELISA kit used in our study could readily detect BLV-specific antibodies in all cows which were also positive for BLV by PCR, but failed to detect reactive antibodies in Chinese women with or without breast cancer. Although information on milk consumption in women participating in our study was not available, our prior study showed a high infection rate of BLV in cows from three provinces where our human samples were collected .The strong association between BLV and human breast cancer would raise serious public health concerns worldwide. Here we provide unambiguous data showing no evidence of BLV in Chinese women with or without breast cancer. Further investigations are warranted to explore the association of BLV with breast cancer in patients with different genetic backgrounds and from different countries as well as their exposure to milk and the levels of infection in dairy herds.


MRI and radiation treatment planning‐CT by utilizing a planning‐MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain.Validating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic‐MRI) and the second on the same day as the planning‐CT (planning‐MRI). The diagnostic‐MRI was deformed to the planning‐CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning‐MRI provided an independent surrogate for the planning‐CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas.For thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning  within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013).The planning‐MRI as a surrogate for the planning‐CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.By utilizing the planning‐ For radiotherapy, this has resulted in improved staging and delineation of the dominant tumor lesion, radiation targets, and organs at risk.For DIRs, several strategies have been developed to characterize and quantify DIR algorithms.22.APatients were enrolled in one of two Institutional Review Board Clinical Trials for delivery of RT boost to identified multiparametric‐MRI (mpMRI)‐defined tumor lesions in the prostate: Hypofractionated External Beam Image‐Guided Highly Targeted Radiotherapy trial NCT01411332); and Lattice Extreme Ablative Dose Radiotherapy for Prostate Cancer trial (NCT01411319). From these studies, 13 patient MRI and CT datasets where both diagnostic and planning MRI datasets were collected on the same MRI instrument were used, details are listed in Table 411332; a2.B3, Field of View: 320 × 320 mm2; slice thickness; 2.5 mm; 72 slices; repetition time 5500 ms and echo time 100 ms.All patients underwent a diagnostic mpMRI study approximately 1 month prior to radiation planning‐CT. The mpMRI includes T2‐weighted, Dynamic Contrast Enhanced (DCE) and Apparent Diffusion Coefficient datasets. All mpMRI sequences were acquired with size and spacing suitable for registration with the planning‐CT. MRI exams were carried out on a Discovery‐MR750 3T‐MRI . For the purposes of this study, only the T2‐weighted sequence was used. The axial T2w‐MRI has a resolution 1.25 × 1.25 × 2.5 mmThe diagnostic‐MRI was used to delineate the dominant lesion(s) in the prostate and provide targets for MRI‐Ultrasound fused prostate biopsyThe planning‐CT and planning‐MR imaging studies were acquired 2–4 weeks following the diagnostic‐MRI. Significant effort in patient positioning and bowel/bladder preparation was undertaken for all datasets to reduce prostate deformation. Specifically, instructions on diet were as follows: magnesium citrate taken the evening before and an enema two hours before the planning‐CT/MRI. The patients were positioned supine with legs placed in a cushion to ensure reproducible positioning of the pelvis. To determine if changes in bladder and rectum volumes impact the quality of the registrations, these structures were contoured on both diagnostic and planning MRI datasets, details are listed in Table Following the planning‐CT, a planning‐MRI was collected, typically within an hour to maximize similarity between these datasets. The planning‐MRI exam consists of a T2‐weighted study, a T2* fast gradient‐echo study for visualizing the gold fiducials and several other imaging studies that are not utilized in this analysis. The T2‐weighted study is collected with identical parameters as the diagnostic‐MRI study. The T2*‐weighted study, MERGE™, is acquired with the same size and spacing to match the T2‐weighted datasets.2.CRigid registrations were performed from the diagnostic‐MRI (moving) to the planning‐CT and from the planning‐MRI (moving) to the planning‐CT, as shown in Fig. The diagnostic‐MRI was deformed to the planning‐CT utilizing a commercially available algorithm  which synthesizes a DIR from local rigid registrations using a Gaussian mixing model to spatially weight the contributions of each alignment, no image similarity metrics are employed by the DIR. Deforming the diagnostic‐MRI to the planning‐CT begins with a rigid alignment over the area of interest. Following this procedure, manual local registrations were obtained between the two datasets by aligning key points throughout the areas of interest, which provided initial conditions for the DIR algorithm, as shown in Fig. 2.Dt‐test with a 95% confidence limit. Pearson correlation is an often used same‐modality similarity metric which is appropriate because the diagnostic and planning images are acquired with the same protocol. NMI is also computed because of its widespread use as a metric for registration optimization and evaluation.The T2‐weighted acquisition from the planning‐MRI study was employed as the surrogate for the planning‐CT for analysis of both the rigid and DIR of the diagnostic‐MRI to the planning‐CT. Agreement between the two MRI datasets was scored using intensity‐based metrics including Pearson correlation and normalized mutual information (NMI) and significance was determined by utilizing a Student's 3Alignment of planning‐MRI to planning‐CT was confirmed to be submillimeter by measuring the residual error of fiducial location between the planning‐MRI and the planning‐CT. This important result demonstrates that the planning‐MRI may be used as a surrogate for the planning‐CT when evaluating the diagnostic‐MRI to planning‐CT deformable registration. The average residual error after fiducial registration was 0.78 mm ± 0.60 mm for the 13 patients, full results are shown in Table P‐value 0.009) within the expanded area, similar findings were found with the Pearson correlation. Figure Consistent findings were made across all comparisons with the DIRs demonstrating substantial improvements over rigid registrations. This difference was significant across all structures studied in 11 out of 13 patients utilizing both NMI and Pearson correlations. Utilizing the NMI, the DIRs were superior to the rigid registrations demonstrating a 27.37% improvement (To investigate regional results of the registrations, the combined and expanded structures were implemented. The combined structure had 10 out of 13 patients with improved NMI, 30.0% average, with similar results for Pearson correlations. Similarly, the expanded structure had 11 of 13 patients with improved NMI, 27.37% average, with similar results for Pearson correlations. Changes in bladder and rectal volumes between the diagnostic and planning datasets did not correlate with NMI or Pearson metrics for either rigid or DIR registrations. To summarize, the deformable registrations were superior to the rigid registrations in all structures and significantly better in all but the proximal seminal vesicles.4CT to CT DIRs are increasingly being relied upon to map crucial information between datasets. The ability to extend these DIRs into the multimodality setting would be enthusiastically received by the radiation oncology community, if it can be done with accuracy and confidence. Currently, MRI is heavily utilized to aid in the contouring of target and OAR volumes on planning CT studies.The characterization of multimodality rigid and DIRs in this study rely on the planning‐MRI's ability to act as a surrogate for the planning‐CT, thus allowing MRI to MRI comparisons to be made. Due to the fiducials being visible on both datasets, the short time between the two acquisitions, the bowel and bladder preparation and the reproducible positioning of the patient, the registration between the planning‐MRI and planning‐CT is of exceptional quality; less than 1 mm on average, this is demonstrated in Table By review of the registrations and by the correlative metrics determined by the study, both rigid and DIRs were of high quality. The DIRs resulted in higher correlation metrics than the rigid registrations, but the rigid registrations were also well‐matched likely due to the focus on reproducible patient setup. For the rigid registrations, there was variability in the correlation metrics for different structures with the penile bulb and the proximal seminal vesicles having the lowest correlations. This may be explained by noting that the prostate was the focus of the rigid registrations and that the registration quality of the bulb and the seminal vesicles are considered to a lesser extent than the prostate. The increased accuracy of the deformable registrations may be explained by the rigid registration only being able to optimize the registration on a small anatomical area. However, improvements were also noted within the prostate with DIR. While prostate is the focus of the work presented here, other body sites may also benefit from this approach. Specifically, sites where MRI is often incorporated into the treatment planning process and where rigid registrations are frequently suboptimal; abdomen, head and neck, brain pre/postsurgery, etc.The DIR validation technique described in this manuscript has several unique attributes. The data employed were collected from protocol patients and do not use simulated images or images of artificial materials. This has the benefit of testing the DIR algorithm using images collected on humans using the equipment present in the clinic, thus reflecting the clinic workflow. Another advantage is that none of the datasets are simulated or altered prior to DIR, thus eliminating any potential issues of utilizing artificial materials or simulated data. One challenge with this validation technique is that it lacks a known DVF to compare the resulting deformable registration to and instead relies on correlations between datasets. While a known DVF is a robust solution, the correlation metrics implemented here share the ability to evaluate registration accuracy across any region that is defined by the user, albeit not pixel by pixel. The emergence of multimodality image deformable registrations holds great promise and will facilitate a more seamless integration of MRI and other imaging modalities into the RT planning process among other applications outside of radiation oncology.In order for multimodality DIRs to be widely adopted, robust validation of these algorithms is necessary. This unique method of validation of multimodality registration utilizing a planning‐MRI as a surrogate complements existing validation methods.Sara Pirozzi and Jon Piper are both employees of MIM Software Inc., which created the deformable algorithm used in this work. Jon Piper also has an ownership interest in MIM Software Inc.


At first glance, it might surprise when an editorial series in a scientific journal asks how science will help to create companies and jobs. However, a recent ‘Nature’ editorial has encouraged ‘research leaders in the United States and elsewhere to address the needs and employment prospects of taxpayers who have seen little benefit from scientific advances’ , and a UK government report estimated that antibiotic‐resistant infections kill 700 000 people each year worldwide (https://amr-review.org/). An increase to worldwide 10 million deaths per year is projected for 2050 if antibiotic resistance continues unabated (https://amr-review.org/). Therefore, curbing antibiotic resistance is a major public health priority as also dramatically formulated by the World Health Organization (http://www.who.int/mediacentre/news/releases/2014/amr-report/en/). The media report cases of patients dying in local hospitals from hitherto relatively harmless pathogens if they turn out to be antibiotic resistant and drastic surgical interventions remain the only option. Therefore, ‘Alternatives to Antibiotics’ became research calls of major grant organizations and the title of international conferences.The public health incentive for alternatives is indeed high: the Centers for Disease Control and Prevention (CDC) calculated 23 000 deaths per year in the USA attributed to infections with antibiotic‐resistant bacterial pathogens  to limit antibiotic use in farm animals. FDA now bans the use of ‘medically important’ antibiotics  as antibiotic growth promoters (AGP) in animal husbandry. FDA has also launched discussions to change the status of antibiotics for disease prevention in farm animals from an over‐the‐counter to a veterinary prescription status. Until recently, a farmer could buy a 50‐kg bag of antibiotics in a veterinary feed supply store without a prescription while a personal use of 200 g antibiotic needed a prescription by a doctor. According to a recent FDA report, 62 per cent of antibiotics given to animals in 2015 were medically important for human health  and could thus contribute to the selection of antibiotic‐resistant pathogens particularly when used at subtherapeutic doses. The amounts of antibiotics used in agriculture are huge: 17 million kg of antibiotics were used in farm animals compared to an estimated 4 million kg in human per year in the USA. A major part of these antibiotics is given in small doses to healthy chicken, cattle and pigs to accelerate weight gain, or in slightly larger doses  to healthy animals for preventing infections. With the ban on antibiotics as growth promoters and the proposed regulation to put disease prevention under prescription, the FDA hopes to see less selection pressure for pathogens to develop antibiotic resistance. It is currently not clear whether the FDA ban is binding or only a recommendation. In the USA, change might also be encouraged by consumer groups who put pressure on fast food restaurants to use meat from chicken raised with minimal or no antibiotics and who publish reports about compliance by the different companies.In January 2017, the US Food and Drug Administration (FDA) set new rules . In the animal feed industry, nutritional supplements is a very active and growing field, many new products were launched, and these activities have created an increasing demand for microbiologists to develop alternative growth promoters for farm animals. One approach would be to study the impact of in‐feed antibiotics used at subtherapeutic (subinhibitory) concentrations on the gut microbiome of farm animals  and by the US Department of Agriculture and formulated in a conference report, microbiome research could play an important role in business development  and on the other hand probiotics that reduce the pathogenicity of opportunistic infectious agents . As the animal feed industry in Europe cannot claim for probiotics antimicrobial effects against pathogens – such products would fall under drug regulation – probiotics are currently marketed for growth‐enhancing benefits and as ‘gut flora stabilizer’. Adisseo, number three of the animal feed industry, has just launched a Campylobacter jejuni colonization and increased clostridia in the crop without lowering the pH of the intestinal digesta. A blend of organic acids and essential oils stimulated body weight gain and decreased E. coli, while lactic acid increased Lactobacillus in the chick ileum.Feed supplements are not limited to probiotics and prebiotics, other compounds are increasingly explored for growth promotion, particularly formic acid in protonated form as an ‘acidifier’ of the intestinal tract in chicken possibly reducing diarrhoea impact. Also chemical companies like BASF are active in that field investigating short‐chain fatty acids for nutrition and gut microbiota modulation. In chicken, formic acid reduced et al., et al., Beyond the animal feed supplement field, microbes and microbial compounds are investigated as antimicrobial agents. These approaches include bacteriophages, phage lysins and bacteriocins that directly kill bacterial pathogens by viral infection (phages), enzymatic action (phage lysins) or lytic action (bacteriocins). As treatment and prevention of bacterial infections with these products would represent medical activities, they will fall under drug legislation and need rigorous proof of efficacy, hence substantial financial investments including further job opportunities for microbiologists. As clinical trials have not yet provided a sound support for these concepts as antibiotic alternatives, investments are only increasing slowly and so far only small biotech companies are active in that medical field . Job opportunities will increase when the first successful clinical trials with phage products are published. Notably, a recent controlled clinical trial with phage therapy underlined the need for microbiome research to identify suitable bacterial target pathogens for phage therapy , different bifidobacteria or the yeast Saccharomyces boulardii. These probiotics generally do not re‐colonize the disturbed gut microbiome, but were designed to restore the physiological gut microbiome composition. The mechanism of probiotic action is frequently not clear and defining these processes with microbiome research could lead to ameliorated probiotic strains. Empirically, doctors in some European countries prescribe an Enterococcus faecium probiotic as adjunct to antibiotic treatment to prevent gut microbiome disturbances. Following the success of faecal transplantation in Clostridium difficile patients and building on earlier clinical trials with synthetic defined bacterial strain mixtures, bacterial cocktails for re‐colonization of a gut microbiome disturbed by intensive antibiotic treatment like in C. difficile infections are intensively explored. In fact, oral antibiotic treatment has not only disturbing effects on the gut, but also on other body sites like the vaginal microbiome which can potentially also be alleviated with probiotic approaches. The current trends in microbiome research reinforce the high potential of microbiological research for translation into practical application leading to new companies, extended activities in existing companies and job creation for microbiologists mastering microbiome research as well as classical microbiology. Bacteriology has thus maintained its eminent role as both a basic and applied branch of science, with important impacts on medicine and industry as predicted in a now 100‐year‐old editorial announcing the genesis of a new science . Meta‐analysis suggests that the consumption of specific probiotic strains is associated with lower rates of AAD in children and adults, but not in elderly patients. The studies were of heterogeneous quality, differed for antibiotic type used and probiotic applied. Most studies in that field relied only on a handful of probiotic bacteria, mainly lactobacilli (None declared.


In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone. The purpose of the present analysis was to evaluate its prospective performance.The evaluation of vigiRank was based on real‐world signal detection in VigiBase. In May 2014, vigiRank scores were computed for pairs of new drugs and WHO Adverse Reaction Terminology critical terms with at most 30 reports from at least 2 countries. Initial manual assessments were performed in order of descending score, selecting a subset of drug‐adverse drug reaction pairs for in‐depth expert assessment. The primary performance metric was the proportion of initial assessments that were decided signals during in‐depth assessment. As comparator, the historical performance for disproportionality‐ guided signal detection in VigiBase was computed from a corresponding cohort of drug‐adverse drug reaction pairs assessed between 2009 and 2013. During this period, the requirement for initial manual assessment was a positive lower endpoint of the 95% credibility interval of the Information Component measure of disproportionality, observed for the first time.P < .05).194 initial assessments suggested by vigiRank's ordering eventually resulted in 6 (3.1%) signals. Disproportionality analysis yielded 19 signals from 1592 initial assessments (1.2%; Combining multiple strength‐of‐evidence aspects as in vigiRank significantly outperformed disproportionality analysis alone in real‐world pharmacovigilance signal detection, for VigiBase. The data are taken from VigiBase, the WHO global database of individual case safety reports.Since 2014, this process currently consists of 3 steps that are reiterated roughly every 6 months. In the first step, a scope is selected that determines a base list of drug‐ADR pairs to consider. For drug‐ADR pairs on this list, vigiRank scores are computed  and WHO Adverse Reaction Terminology critical terms. All in‐depth assessments were completed within 15 months of the initial data screen, which took place in May 2014.The primary performance metric was defined as the proportion of drug‐ADR pairs subjected to initial assessment that eventually resulted in a signal. The secondary performance metric was the proportion of initial assessments that were deemed interesting enough to warrant in‐depth assessment.025).The outcomes observed for vigiRank were compared to corresponding historical metrics from 2009 to 2013, when first‐pass screening of VigiBase relied on disproportionality analysis applied in quarterly database screens. One filter used during this period closely resembles the set‐up used for vigiRank and so was used as comparator. This filter identified pairs of new drugs and WHO Adverse Reaction Terminology critical terms that (1) were reported at most 30 times from at least 2 countries and (2) attained, for the first time, a positive lower 95% credibility interval endpoint of the Information Component (IC3P < .05 using Fisher's exact test). The 6 vigiRank signals came out of 18 in‐depth assessments, corresponding to 9.3% of the initial assessments. The corresponding proportion for disproportionality analysis was 215 of 1592 .All results are presented in Figure 4This study provides empirical support that vigiRank, a recently devised method that simultaneously accounts for multiple strength‐of‐evidence aspects, offers higher real‐world signal detection performance than disproportionality analysis alone. This corroborates our earlier retrospective evaluationThe main strengths of our study are its prospective nature for the vigiRank cohort, its overall size, and its independence of the data used for the development of vigiRank. Its main limitation is that there are several other factors varying between the two study periods beyond the choice between disproportionality analysis and vigiRank. Alongside the introduction of vigiRank in 2014 came a new operational framework for initial signal assessments at the UMC, which involves a larger and more heterogeneous group of staff for shorter and more intense periods. In addition, there has been staff turnover during the 5‐year period, including the in‐house medical doctors ultimately responsible for determining which combinations that merit in‐depth assessment. However, all of these factors should primarily affect decisions whether to perform in‐depth assessment and have little influence, if any, on signal classifications in those assessments. Since the observed difference in performance relates to the latter, these factors are unlikely explanations.Although vigiRank offers great improvement overall compared to disproportionality analysis, it does not generate a higher rate of initial assessments being sent for in‐depth assessment by clinical experts. A possible explanation is that vigiRank proposes a set of drug‐ADR pairs for initial assessment that are of generally superior quality and so subconsciously raises the bar for what is going to be subjected to in‐depth assessment. The much more thorough and therefore labour‐intensive approach of the in‐depth assessments may be a contributing factor for such behaviour. Nevertheless, we are confident that it is the overall efficiency that is of real importance and the one that therefore should be measured.025 exceeded 0 for the first time. For vigiRank, we reviewed pairs in the order of descending scores. This could explain part of the substantial gain in performance, which exceeds that observed in the previous retrospective comparison.It is important to note that our comparison does not contrast the vigiRank score and IC measure in isolation but reflects specific applications for practical use. For the IC, we highlighted drug‐ADR pairs for which ICvigiRank is different from the majority of other efforts to advance statistical signal detection in pharmacovigilance. Most developments seem to have been made in the area of multivariate methods aiming to account for possible confounders, such as age or indication for use, to adjust and generalise the crude reporting associations offered by disproportionality analysis.A challenge with a more complex method like vigiRank compared to basic disproportionality analysis is that it becomes more difficult to apply in other data sets. Adaptation could be made at different levels, where refitting the underlying predictive model in the target database is the most ambitious and most likely to succeed.In conclusion, combining multiple strength‐of‐evidence aspects as in vigiRank significantly outperforms disproportionality analysis alone in real‐world pharmacovigilance signal detection, for VigiBase. This is a first success story in need of independent verification, but the substantial improvement observed warrants careful consideration by anyone seeking to improve their statistical signal detection for individual case reports.The authors declare no conflict of interest.


In birds and mammals, mobbing calls constitute an important form of social information that can attract numerous sympatric species to localized mobbing aggregations. While such a response is thought to reduce the future predation risk for responding species, there is surprisingly little empirical evidence to support this hypothesis. One way to test the link between predation risk reduction and mobbing attraction involves testing the relationship between species’ attraction to mobbing calls and the functional traits that define their vulnerability to predation risk. Two important traits known to influence prey vulnerability include relative prey‐to‐predator body size ratio and the overlap in space use between predator and prey; in combination, these measures strongly influence prey accessibility, and therefore their vulnerability, to predators. Here, we combine community surveys with behavioral experiments of a diverse bird assemblage in the lowland rainforest of Sumatra to test whether the functional traits of body mass (representing body size) and foraging height (representing space use) can predict species’ attraction to heterospecific mobbing calls. At four forest sites along a gradient of forest degradation, we characterized the resident bird communities using point count and mist‐netting surveys, and determined the species groups attracted to standardized playbacks of mobbing calls produced by five resident bird species of roughly similar body size and foraging height. We found that (1) a large, diverse subcommunity of bird species was attracted to the mobbing calls and (2) responding species  tended to be (a) small (b) mid‐storey foragers (c) with similar trait values as the species producing the mobbing calls. Our findings from the relatively lesser known bird assemblages of tropical Asia add to the growing evidence for the ubiquity of heterospecific information networks in animal communities, and provide empirical support for the long‐standing hypothesis that predation risk reduction is a major benefit of mobbing information networks. At four forest sites spanning a gradient from intact to secondary forest in lowland Sumatra, Indonesia, we conducted intensive point count and mist‐netting surveys to characterize the resident bird communities. We further conducted standardized playback trials using the simultaneous mobbing calls of five common prey bird species inhabiting these forests in combination with the playback of a common predator (an owl species), and characterized the species responding to these playbacks by quantifying their approach behaviors. The five species producing mobbing calls in the playback stimulus are generally similar in body size and foraging height, while the selected owl species is known to be a target of mobbing by these and other prey species. We characterized the distribution of body size (measured by body mass) and foraging height among responding and nonresponding species, and analyzed whether these functional traits predicted species’ tendency to exhibit attraction behaviors to mobbing calls given their presence in the bird communities.22.1We conducted fieldwork at four forest sites located within two locations in the rainforests of lowland Sumatra Figure . The Way2.2We conducted variable‐radius point count . The owl playback using territorial calls and model presentation simulated the perched presence of the Sunda scops‐owl Otus lempiji, an important predator of the forest mid‐storey on large insects and small vertebrates, including birds, in lowland Sundaland  and October (DEG1 and DEG3 only) of 2011; we did not conduct mist‐netting surveys at DEG2  because we did not use a 15‐m distance band during point counts.2.4We conducted standardized mobbing playbacks in June DEG 1–3) and July (PRIM) of 2011, between 08:30 and 13:00 hr on days without rain or strong wind. At each study site, we delineated a 125‐ha  plot with 30 sampling points spaced 250 m apart at which we conducted playbacks of mobbing plus owl calls along with owl model presentation and behavioral observations  the owl model and (2) a camouflaged speaker for the owl vocalization (both on top of a 3‐m‐long pole), and (3) a second camouflaged speaker for mobbing calls in vegetation 1–2 m away from the pole at ~2 m above ground facing the owl model. We connected each speaker via extension cords to an iPod player and controlled them remotely. We considered the pole as the playback center, and demarcated a 15‐m distance radius from the pole in four ordinal directions with colored ribbons to provide visual aid for behavioral data collection. After setting up the playback system, we retreated to 10 m away from the owl model, waited for 2 min to allow any possible disturbance caused by our movement to go away, and conducted playbacks nonstop for 10 min. During the two‐minute preplayback period, we also recorded the identity and number of birds within the 15‐m radius to characterize the “baseline” birds, that is, birds that were naturally near the playback center. We standardized the playbacks at natural and undistorted volumes at a 10‐m distance.During the 10‐min playbacks, we recorded the presence/absence of species approaching to within 15 m from the playback centers. Therefore, species approaching to within 15 m from the playback centers were considered as exhibiting an attraction response to the mobbing calls. Data on species identity and abundance were collected mainly by one observer , and were supplemented by a second observer (FH) who, while taking behavioral observations on bird individuals on voice recorders, was able to opportunistically record the identity of species and number of individuals. In all, we conducted 101 playback trials.2.5We obtained data on the metrics of body mass (in grams) and foraging height (in meters) for all bird species recorded in our study from the EltonTraits 1.0 data base  for analysis because we conducted mobbing playbacks during the avian breeding season of the Northern Hemisphere when winter migrants such as some leaf warblers and flycatchers were not present. We also removed from analysis the five species represented in the mobbing playback file to focus our analysis only on the heterospecific use of social information. We measured the “tendency to exhibit attraction behaviors to mobbing calls” at the level of study sites and for each species. For a given study site and species, we defined this tendency as the probability of the species being attracted to mobbing calls during at least one playback trial (measured by its presence/absence within 15 m from the playback center) given its presence in the bird community at the site (measured by its detection/nondetection in the combined survey data sets from point counts and mist‐netting). Similarly, we defined the tendency to be distributed close to point count stations at the level of study sites and for each species, as the probability of the species being recorded within 20 m from a point count station at least once given its presence in the bird community at the site.To characterize the distributions of body mass and foraging height of bird species, we plotted the kernel density curves of these two functional traits among responding and nonresponding species and displayed the trait positions of the five species producing the mobbing calls. To test how these traits predicted species’ tendency to respond to the mobbing calls, we constructed two‐tiered hierarchical models that were based on the maximum‐likelihood estimation approach. We used two complementary trait measures: raw trait values directly extracted from EltonTraits 1.0 data base  to four ; this model tier thus assumed independence among study sites in terms of each species’ attraction status. The two model tiers therefore combined to mathematically describe the number of sites where a species was expected to respond to mobbing calls, out of all the sites at which it was present. We estimated the effect of the covariates in the first‐tier models using the maximum‐likelihood approach.The first tier of the hierarchical models defined a species’ probability, at the level of study sites, of responding to the mobbing calls (given its presence in the community), p followed a linear relationship with m, h, along with their respective quadratic terms  for the first model tier and used model selection based on Akaike's information criterion (AIC) to select the best model  or far away from (>20 m) point count stations. We plotted the kernel density curves of body mass and foraging height among species distributed close to and far away from point count stations, displaying the trait positions of the five species producing the mobbing calls. We additionally conducted the hierarchical model analysis described above to test how these traits predicted species’ tendency of being close to point count stations, again using both raw and relative trait values. We conducted all analyses in R 3.2.3  from a total of 71 species (Table S2). Responding species spanned 22 of the 33 families detected, were predominantly members of the order Passeriformes, but also included members of the orders Cuculiformes, Trogoniformes, Coraciiformes, and Piciformes. Thus, the component of the bird community that utilized the mobbing calls provided in our study accounted for ~45% of the bird community detected in our surveys in terms of the number of species, and covered a wide taxonomic breadth.The vast majority of responding species were of relatively small body mass . Specifically, a species’ tendency to respond to mobbing calls was negatively correlated with body mass and exhibited a downward‐facing parabolic relationship with foraging height that peaked at the intermediate height of 14.03 m, just above the “mid‐high” stratum and coinciding with the foraging height of 4Combining community surveys with playback‐based behavioral manipulations, we show that nearly half (~45%) of the prey bird species at our study sites in the lowland rainforest of Sumatra were attracted to the heterospecific mobbing calls of five small‐bodied, mostly under‐ and/or mid‐storey bird species. Moreover, these species represented a nonrandom subset of the wider resident bird community; similar to the five species producing the mobbing calls used in our playbacks, responding species tended to be small‐bodied foragers of the under‐ and mid‐storey forest strata Figures  and 3. SOur results provide empirical support for the hypothesis that the reduction in future predation risk is likely a major benefit and evolutionary driver of the heterospecific attraction to mobbing calls . We note also that we did not quantify birds’ mobbing behavior here, which are distinct from birds’ attraction to mobbing calls and may well be sensitive to variation in vegetation structure caused by forest degradation (Hua & Sieving, Two other caveats to our study design are also worth noting. First, our study used the simultaneous mobbing calls of five species that, despite sharing largely similar traits, spanned a range of trait values (particularly in terms of foraging height; Figure Our study demonstrates the existence and diversity of a risk‐sharing bird community in the lowland rainforest of Sumatra, where a large number of species spanning a large number of families readily use heterospecific social information in the form of mobbing calls. This finding for the comparatively lesser known tropical ecosystems of Asia (Goodale & Kotagama, Authors claim no conflict of interest.All original data are provided in the Supporting information. Click here for additional data file.


UK based on ∼1600 rain gauges from three different data sources. This includes tipping bucket rain gauge data from the UK Environment Agency (EA), which has been collected for operational purposes, principally flood forecasting. Significant problems in the use of such data for the analysis of extreme events include the recording of accumulated totals, high frequency bucket tips, rain gauge recording errors and the non‐operation of gauges. Given the prospect of an intensification of short‐duration rainfall in a warming climate, the identification of such errors is essential if sub‐daily datasets are to be used to better understand extreme events. We therefore first describe a series of procedures developed to quality control this new dataset. We then analyse ∼380 gauges with near‐complete hourly records for 1992–2011 and map the seasonal climatology of intense rainfall based on UK hourly extremes using annual maxima, n‐largest events and fixed threshold approaches. We find that the highest frequencies and intensities of hourly extreme rainfall occur during summer when the usual orographically defined pattern of extreme rainfall is replaced by a weaker, north–south pattern. A strong diurnal cycle in hourly extremes, peaking in late afternoon to early evening, is also identified in summer and, for some areas, in spring. This likely reflects the different mechanisms that generate sub‐daily rainfall, with convection dominating during summer. The resulting quality‐controlled hourly rainfall dataset will provide considerable value in several contexts, including the development of standard, globally applicable quality‐control procedures for sub‐daily data, the validation of the new generation of very high‐resolution climate models and improved understanding of the drivers of extreme rainfall.Sub‐daily rainfall extremes may be associated with flash flooding, particularly in urban areas but, compared with extremes on daily timescales, have been relatively little studied in many regions. This paper describes a new, hourly rainfall dataset for the  Maraun et al.  for eight durations between 1 h and 8 days  procedures. These procedures identify potentially suspect values, particularly extremes, based on identified problems associated with sub‐daily data. The combined, quality‐controlled dataset is then used to provide a climatology of the main features of hourly extremes for the period 1992–2011. This climatology describes the frequency, intensity, seasonality and diurnal cycle of extreme hourly rainfall events using AM, 22.1The historical dataset used in this analysis has been constructed from three sources. The first of these is the UK Met Office Integrated Data Archive System (MIDAS), which may be downloaded from the British Atmospheric Data Centre . Where the TBR totals are less than (greater than) 25 mm and are within ±2 mm (±8%) of the check gauge, then data are classified as ‘good’. Where differences exceed these tolerances, then data are either classified as ‘suspect’ or, in serious cases, are deleted and marked as missing. Data is also marked suspect in instances where a site inspection has indicated some irregularity with the rain gauge. The results of these procedures are provided as metadata and, in most cases  are identified as good, although it was noted that this flag is applied to validated daily or monthly accumulations. In this analysis, all data classified as suspect  were treated as missing, whilst all good and ‘unchecked’  data were subjected to the additional QC procedures described below. Most of the suspect amounts (∼92%) are 0.2 mm or less  also suggests regional differences in recording and/or validation procedures and confirms the need for additional, consistent QC.2.3et al., There is no standard approach for the QC of sub‐daily rain gauge data. A suite of procedures are therefore used here  followid Rahimi  unless t2.3.2A small number of negative values mainly occurring as a consequence of erroneous coding of missing data were removed and replaced with correct missing data markers.http://www.metoffice.gov.uk/public/weather/climate‐extremes/#?tab =climate Extremes for a list of UK climatological extremes). However, this does not necessarily mean that larger totals identified in this dataset are erroneous as it comprises a larger network of gauges and therefore may capture additional, localized intense events. All hourly totals exceeding this amount by 20% or more were identified and treated immediately as suspect (QC1). In this way, obvious recording errors, such as incorrect coding of missing data, recording malfunctions, persistent high‐frequency tipping and obvious accumulations over longer periods, could be quickly highlighted and denoted as suspect. The same process was applied to daily (24 h) totals using the corresponding record of 279 mm, which was compared with accumulated hourly rainfall over the period 0900–0900 (QC3). Hourly/daily totals exceeding these records by less than 20% were further assessed by checking against the raw TBR data and metadata (where available) and along with all other data by using the additional checks described here and in list 2).The record UK 1 h rainfall total is reported as 92 mm  along with the preceding dry period. Secondly, as daily accumulations were noted to occur in continuous sequences, consecutive daily rainfall occurrence at 0900 and 1200  and following 23 h without recorded rainfall were also treated in the same way (QC5).Daily (24 h) accumulated values were noted to be most likely to occur at 0900 and 1200 and were identified using two procedures. Firstly, hourly totals at 0900, which exceed two times the mean daily rainfall intensity for that month, Duplicate rainfall totals in consecutive hours  were recorded as suspect (QC7).Values automatically treated as suspectAs the most intense 1 h rainfall was subsequently noted to generally occur in summer and early autumn, values in the period October to April exceeding 80% of the record 1 h total were flagged (QC2).Potential monthly accumulated values were identified as months with only one hourly value in excess of two times the mean daily rainfall intensity for that month (QC6).Specific examination was also undertaken of intense events that occurred frequently at specific dates .Values are only treated as suspect if additional evidence is available either from raw TBR data and metadata or flags from multiple QC tests:After the TBR data had been accumulated to hourly totals, the combined dataset from all three sources was subjected to a further sequence of independent, single rain gauge checks. Firstly, potentially erroneous hourly values were identified using a number of procedures and examined. These may arise as simple recording errors or may be values derived from accumulations over longer time periods. It is important to note that some of these procedures should not be considered independently but be used to provide accumulated evidence for potentially suspect hourly totals. We therefore sub‐divide the tests into two categories:ad hoc comparison with nearby gauges and documentary sources of information such as the Royal Meteorological Society's Weather Log and the event chronology provided in Eden (Rmed) and Extreme Rainfall Alerts (ERA) events respectively as described below] is provided in Figures S3 and S4. This demonstrates that whilst these statistics are only affected for a minority of rain gauges , the measures taken are essential to avoid erroneous analysis of some features of local and regional climatological and hydrological extremes.In the absence of multiple sources of evidence, hourly totals identified by only one test in list 2 were retained to avoid legitimate values being discarded. Manual inspection of TBR data and metadata is a time‐intensive process and so was limited here to the final gauge selection described below based on data length and completeness. This included additional strategies of  in Eden . The relA further important point to note is that the QC of observed data should be considered a multi‐facetted process. Ideally, the above checks should be combined with local/regional knowledge of climatological processes and basic climatological data analysis. For example, the subsequent assessment of extremes detailed in this paper identified a significant seasonality in hourly extremes in the UK linked with rainfall generating processes that could subsequently be used to identify and investigate totals that were not consistent with this knowledge, for example, intense events occurring in winter. Such process‐based knowledge may therefore contribute to QC procedures.2.3.3Although this research is focused on assessing the sub‐daily extreme rainfall climatology of the UK, it was also necessary to undertake some assessment of dry spells in the rain gauges as not all gauge malfunctions are readily identifiable in the TBR data, which may lead to the erroneous recording of zero values and indicate that a gauge is suitable for analysis over a given period when in fact this is not the case. The identification of the most egregious errors in terms of dry spells in the record is important as failure to do so may lead to seasonal and annual statistics being derived from unrepresentative, small samples. Unfeasibly long dry spells (e.g. > 6 months) were identified for some rain gauges and could be readily treated as missing. In some instances, significant dry sequences occurred at the start of the record, suggesting that the gauge start date had been incorrectly recorded and populated by zero rainfall values. In other cases, such spells were noted at the end of the gauge record, which suggested that it had ceased to operate some time prior to the termination of the record. Such ‘terminal’ dry spells were treated as missing data (QC9). All other instances where no rain was recorded  for periods of greater than 1 month were flagged for further investigation (QC10), with an additional flag to identify the most extreme spells of longer than 45 days , alongside the percentage correct statistics described below.2.4et al., sr) was used to highlight errors in the hourly dataset by comparing each rain gauge and its corresponding 5 km grid cell. Figure sr of less than 0.9 were investigated, often revealing a very obvious error that could provide further evidence for the single gauge QC checks.As a check on the overall quality of the data, the hourly records for each gauge were accumulated over the period 0900 on the day recorded to 0900 the next day and compared with the UKCP09 5 km gridded daily rainfall dataset , the proportion of days in which it rains in the daily record but not the aggregated hourly record (P10), the proportion of days that it rains in the hourly record and not the daily record (P01) and the proportion of days where it does not rain in either record (P00). P00 + P11 therefore shows how concordant the two records are. Wilks  indicate2.5The total dataset comprises 1638 gauges, and as shown in Figure 2.6Rmed statistic (median AM) was determined. Rmed corresponds to an RP event of 2 years  and is robust to outliers in the AM time series. It has been used to assess changes in UK daily rainfall extremes ; for n = 3, the largest 60 events would be considered (hereafter n3) etc. It thus provides more comprehensive information than the AM series as all extreme events may be represented. The declustering algorithm described by Ferro and Segers  are recorded in this dataset , which, coupled with their highly localized nature, makes it difficult to analyse their spatial occurrence based on measurements at discrete points afforded by a rain gauge network. However, although Hurford et al.  yields a mean day of year for events, θ‾, and the concentration of the seasonal distribution, r‾. A value of the latter close to 1 indicates that events usually occur at the same time of the year, whereas a lower value indicates that seasonality is weaker, and consequently, the value of θ‾ is less representative of the distribution. Values of θ‾ and r‾ for each of the extreme rainfall regions are shown in Table n1 events has the strongest seasonality in eastern regions – North East, East Anglia, Humber, Forth and South East – r‾ ≥ 0.65. For these regions, the peak time of year is also associated with the highest magnitude events as shown in Figure n1 events for the North East region (14 gauges). In contrast, the weakest seasonality occurs in western regions and northern Scotland as illustrated by the North Highland region . The associated histogram clearly demonstrates the higher relative frequency of events in autumn and winter in this region. The annual distribution of n1 events for all regions is shown in Figure S9. The mean time of occurrence, θ‾, of n1 events is in summer for all regions except North Highland, for which mean occurrence is in mid‐autumn, ∼50 days later than the earliest regional mean occurrence. These characteristics broadly reflect the monthly MWHI, also shown in Figure S5, which in the southern and eastern regions is at a maximum in late summer and is lowest during winter and early spring. The evidence presented by Rust et al.  as these are more likely to occur in autumn  in Figure n1 in the eastern region possibly extending later into the day. For n5, there are a sufficiently large number of events to examine the diurnal cycle of hourly rainfall extremes for individual extreme rainfall regions. This is not shown for conciseness, but for some western and northern regions , the summer diurnal cycle is not present, most likely as a consequence of the mixed origin of events with the inclusion of low intensity, non‐convective rainfall.A brief assessment of the spatial consistency of the diurnal cycle of hourly extreme rainfall across the UK was obtained by examining the same data for three larger regions derived from the amalgamation of the extreme rainfall regions. An eastern region reflecting the area identified previously as having the strongest seasonality for 4The development of methods for the use of neighbouring gauges as an additional check of hourly totals and dry spells. This is however a non‐trivial issue, particularly considering the localized nature of summer storms.The development of a ‘rule base’ that may be used to combine the procedures described in this paper with (1) above to provide an automated QC process requiring minimal manual data checking. This would enable the efficient assessment of the considerable amount of data before 1992 in this dataset.The application of additional assessments of long‐term homogeneity through the use of appropriate statistical tests for break points are required before the analysis of longer‐term trends and variability in sub‐daily rainfall. As Figure The determination of appropriate statistically defined thresholds that may be used to identify the most egregious errors and accumulations in different climatic regimes globally, particularly where national records are not available or for countries with large geographical climatic variability where a single record total may be less appropriate.A new dataset of hourly rainfall for the UK has been produced, combining three different sources of rain gauge data. As with all observational datasets, QC is an essential process before data analysis and wider application. This paper has demonstrated that there may be significant problems associated with raw TBR data, which in the UK, have generally been collected for operational functions rather than for long‐term climatological analyses. We therefore describe a series of QC procedures that may be applied to both TBR data and data accumulated to hourly totals. The quality of the data is checked by accumulating the hourly values to a daily total for comparison with a quality‐controlled gridded daily rainfall product, and only those showing good agreement are retained. These QC procedures focus on the identification of spurious extreme values and may form the basis of a set of standard checks for sub‐daily data. They are not expected to produce a ‘perfect’ dataset, but we consider that they provide a reliable one for the climatological analyses of extreme hourly rainfall. The methodology is best equipped to identify spurious extreme rainfall amounts, but additional procedures may be required for an assessment of moderate rainfall amounts and dry spells. Such additional work may be required before the data is suitable to drive hydrological models for some types of analyses. We consider the next steps therefore to be:A subset of this data was applied to assess the climatology of hourly extremes for the UK, comprising 376 gauges that were near‐complete for the period 1992–2011. AM and percentile approaches were used seasonally to identify a west–east pattern of decreasing extremes from autumn through to spring. This is similar to that observed for daily rainfall accumulations, which are influenced by the prevailing direction of cyclonic weather systems and modified by orography, western and northern areas being associated with orographically enhanced rainfall. In summer this pattern of extremes is replaced by a less well‐defined north–south pattern, when extreme hourly rainfall is less likely to be associated with such mesoscale weather systems and more probably a consequence of local‐scale convection (or convection embedded within such systems).n‐largest events, is most pronounced in eastern coastal regions of England and Scotland and suggests that the risk of flash flooding in urban areas and rapid response river catchments may be greatest in summer in most parts of the UK. However, the spatial distribution of ERA events , which also predominantly occur in summer, albeit representing relatively rare events, indicates fewer of the most intense totals in eastern coastal regions. This seasonality has some similarities to the analysis of daily rainfall extremes presented by Rodda et al.  are noted to be bimodal, with peaks in summer and winter (this may have been a consequence of failing to distinguish between different precipitation regimes across the UK). However, the seasonality identified here has a limited similarity to that noted for daily rainfall by Rust et al. . This suggests a possible diurnal cycle in intense hourly rainfall, which is identified in the occurrence of the n‐largest events in summer, showing a peak frequency in the late afternoon/early evening. This seems to occur relatively consistently across the UK, although a similar cycle detected in spring is most evident once again in eastern coastal regions. This cycle is likely driven by convection that is at a maximum at this time of day, associated with the diurnal peak of temperature, although further investigation is required into the local‐ and large‐scale drivers of intense rainfall. Extreme UK daily precipitation in winter and autumn has been shown to be strongly influenced by synoptic‐scale ‘atmospheric rivers’ . Alternatively, such analyses might include an assessment of the climatology of gauges in relation to known processes. Two examples of the latter from this analysis include the highlighting of high‐intensity events in winter (these events were shown to be most closely associated with summer) and the identification of 0900‐accumulated totals in some rain gauges producing a corresponding morning spike in the diurnal cycle of extreme events. The seasonality and timing of extremes might therefore form part of a suite of essential QC procedures and diagnostic analyses for sub‐daily precipitation data.et al., et al., et al., Typically, regional climate models have been noted to have limited skill in the simulation of UK summer rainfall extremes on both daily  project as a contribution to the Global Energy and Water Cycle Experiment (GEWEX) Grand Challenge on Extremes. This has identified the importance of efforts to undertake new and novel QC algorithms at different timescales  of the three classes of quality‐control metadata for EA gauges for three regions and the number of contributing gauges (n).Click here for additional data file.Figure S2. Relative frequencies of data flagged by the main quality‐control procedures applied to all data.Click here for additional data file.Figure S3. Change in seasonal 1 h Rmed after additional quality‐control procedures.Click here for additional data file.Figure S4. Number of recorded 1 h extreme rainfall alert (ERA) threshold events (≥30 mm h−1) removed by additional quality‐control procedures.Click here for additional data file.Figure S5. Seasonal mean wet hour intensity.Click here for additional data file.Figure S6. Seasonal 99th percentile wet hour amount.Click here for additional data file.Figure S7. Seasonal 99.9th percentile wet hour amount.Click here for additional data file.Figure S8. Frequency of 1 h extreme rainfall alert (ERA) threshold events (≥30 mm h−1) after additional quality‐control procedures.Click here for additional data file.Figure S9. Timing and magnitude of n1 events by extreme rainfall region.Click here for additional data file.


This proof of principle study evaluated the effectiveness and feasibility of a brief motivational intervention, delivered in mental health in‐patient settings, to improve engagement in treatment for drug and alcohol misuse.UK mental health service, with schizophrenia related or bipolar disorder diagnoses, users of community mental health services and also misusing alcohol and/or drugs. Participants were randomised to Brief Integrated Motivational Intervention (BIMI) with Treatment As Usual (TAU), or TAU alone. The BIMI took place over a 2‐week period and encouraged participants to explore substance use and its impact on mental health.A randomised controlled trial using concealed randomisation, blind, independent assessment of outcome at 3 months. Participants were 59 new adult admissions, to six acute mental health hospital units in one BIMI n = 30; TAU n = 29) were randomised, the BIMI was associated with a 63% relative odds increase in the primary outcome engagement in treatment [OR 1.63 ], at 3 months. Qualitative interviews with staff and participants indicated that the BIMI was both feasible and acceptable.Fifty‐nine in‐patients (Mental health hospital admissions present an opportunity for brief motivational interventions focussed on substance misuse and can lead to improvements in engagement. It is both feasible and acceptable for in‐patient staff to engage in‐patient's in brief motivational interventions focussed on their substance misuse, whilst admitted to psychiatric hospitals. Staff who delivered the intervention found the short burst and targeted style a useful method of engaging in‐patients.The findings indicate that the brief motivational intervention, delivered during acute psychiatric admission, led to improvements in engagement in substance misuse treatment.Whilst this is a challenging group of participants to engage, it is clear that it is possible to recruit and retain participants from this client group and primary outcome data were available for 58, highlighting the advantage of a primary outcome measure based on assessment by care coordinators.This was a pilot feasibility study, which supports the need for a larger trial.in‐situ supervision and formed a bridge with care coordinators in the community. However, the availability of such a resource is limited in routine services and adds organisational complexity.Specialist ‘dual diagnosis’ staff delivering the intervention alongside in‐patient staff provided the latter with an additional level of available expertise, informal Further research will need to test whether the intervention impacts significantly to reduce subsequent hospital admissions and substance misuse.Sealing over the experience of relapse was found to predict low engagement with mental health services 6 months after discharge for psychiatric in‐patients Hospital admissions may present a natural window of opportunity for in‐patients to re‐evaluate behaviours that might negatively impact on their health and mental health Of those admitted into psychiatric in‐patient facilities 22–44% have been found to have coexisting alcohol or drug problems The current study sought to evaluate whether a psychiatric hospital admission represents a natural window of opportunity for individuals who misuse substances to be routinely offered treatment, by in‐patient staff, to re‐evaluate drug and alcohol use and become aware of negative impacts on mental health.The study was an open (rater blinded), prospective randomised trial, analysed by intention to treat Brief Integrated Motivational Intervention (BIMI) would significantly improve treatment engagement for alcohol and drug misuse compared to TAU. The secondary hypotheses were that those receiving the BIMI would show greater readiness to change substance use behaviour when compared to those receiving TAU and that the BIMI would be more cost‐effective than the TAU due to reduced service utilisation.The primary hypothesis tested was that Eligible participants were identified by Research staff based on review of care records. A screening measure was completed with care coordinators confirming trial eligibility. Within 2 weeks of admission, once acute symptoms eased, eligible participants were invited to participate. Written information about the study was provided and written consent obtained. A battery of assessments was administered. Upon completion, participants were randomly allocated to the intervention BIMI group (in the context of TAU) or the TAU group (the control group). Just prior to the 3‐month data collection meetings were scheduled for completion of follow‐up assessment battery by blind researchers. Participants were not paid for study participation.A power calculation was carried out based on a previous study The trial used independent central randomisation using a concealed process via e‐mail. The researchers were blind to participant treatment group allocation until all baseline, post‐treatment and 3‐month follow‐up quantitative assessments had been completed. Researchers were unblinded once the 3‐month data collection had been completed. Participants in the BIMI group then completed a qualitative interview.‘Brief Integrated Motivational Intervention’, was offered in the context of TAU, guided by a manual designed for purpose, based on key ingredients in the two early phases of Cognitive‐Behavioural Integrated Treatment The BIMI, a The BIMI was delivered by in‐patient unit staff trained as part of the study working alongside staff from a specialist ‘dual diagnosis’ Trust‐wide service, the COMPASS Programme Treatment as usual was provided by nursing and medical staff on the mental health units in line with in‐patient policies and is regularly monitored by the UK Care Quality Commission. It included assessment and monitoring mental state, provision of medication and stabilisation of mental state.sas version 9.4 . The analysis method for the primary outcome was a proportional odds model. The proportional odds method used included the pre‐ and postintervention periods (L1SATS and L3STATS), included a cumulative log link function and multinomial error, linked within a subject with a random intercept term. Denominator degrees of freedom were derived from the number of subjects. Models used maximum likelihood with adaptive quadrature. The appropriateness of the proportional odds assumption was assessed using the Score Test. Statistical significance was assessed using a conventional two‐sided alpha of 0.05. The secondary outcomes were analysed using analogous generalised mixed models with appropriate link functions and error structures. The ITT principle was used for all analyses. Missing data were excluded as it would be inappropriate to impute it because it is most plausibly missing not at random.Analysis was carried out using Participant contacts with staff providing the intervention were recorded as well as staff supervision time, and these were combined with unit costs Qualitative semistructured interviews with participants after the 3‐month follow‐up point sought to assess satisfaction with the treatment received and perceived processes of change, including helpful aspects of the therapeutic process. A sample of therapists was interviewed using a semistructured interview, individually and in focus groups. These were recorded, transcribed and thematic analysis conducted, steps were taken to ensure reflexivity The primary outcome was engagement with substance misuse treatment whilst in‐patient and with community treatment services at 3‐month follow‐up as reported by care coordinators/primary clinician, using the SATS Stages of Change Readiness And Treatment Eagerness ScaleCurrent readiness to change alcohol and drug use was assessed using the 19‐item Importance‐Confidence Ruler which is a global assessment of motivation and confidence to change assessing two concepts underpinning readiness to change Motivation to change was measured by the Maudsley Addiction ProfileSeverity of Dependence Scale was used to screen for the severity of drug dependence Alcohol Use Disorders Identification Test (AUDIT) a well validated 10‐item self‐report questionnaire was used to investigate alcohol consumption Drug and alcohol use was assessed using a number of complementary measures. The number of days each substance had been used in the past 30 days, and the average amount of use of each drug on a using day was assessed using section B of the Psychological functioning was assessed using three measures: Recovery Style Questionnaire (RSQ) is a 39‐item self‐report measure designed to assess two concepts of recovery from mental health relapses; ‘Integration’ , and ‘Sealing‐over’ . It reliably enables four recovery styles to be classified based on a continuum  ; TAU = 7.19(SD 3.58)] and Confidence to be able to change . There was very little variation between Importance  and Confidence  scores across time and between groups.P = 0.85)] ] Table .P = 0.24)].In supportive analysis, the amount of substance used was explored. At 3‐month follow‐up, 15 participants reported not having used any substances in the past 30 days; 10 of whom were in the BIMI group compared to 5 in TAU Table . HoweverMean baseline scores for the primary substance used for both groups, as rated by the care coordinator, were categorised as ‘abuse’ in the previous 3 months Table . Mean AUP = 0.611)]. The HADS Depression, although not significant, was in line with a modest effect on that outcome suggesting that the TAU had higher depression scores ] ], and the BIMI group as adopting an ‘integration’ style [71.14 (SD 13.37)]. At 3‐month follow‐up, both groups were classified as ‘integration’ (TAU = 70.74 (SD 70.74); BIMI = 71.15(SD 18.97), suggesting that both groups had an acknowledgement of and were making attempts to cope with their mental health problems. Due to the amount of missing data, no further analysis was conducted.Awareness of symptoms and of illness mean scores indicated ‘poor insight’ over time in both TAU groups at baseline and follow‐up. Need for treatment mean scores increased over time for both groups suggesting ‘good insight’ into need for treatment. The difference in means implied a benefit for the intervention. Whilst there were no significant differences, all values pointed towards a benefit from the BIMI Table .n = 19; TAU n = 13) and more participants in contact with assertive outreach teams . At baseline, the number of days in hospital was similar between the groups (BIMI = 13.4 (SD 7.9); TAU=14.5 (SD 9.7). During the post‐randomisation period, in‐patient days were slightly higher in the BIMI group (BIMI = 45.3 (SD 35.1); TAU = 35.8 (SD 30.8). The mean cost of delivering the intervention was £72 (SD £66). The total mean cost of services used postrandomisation by the TAU and BIMI group was £15 698 (SD £12 632) and £18 651 (SD £15 580) respectively. Adjusting for baseline costs, the BIMI group had costs that were on average £3279 higher postrandomisation than the TAU group . One participant in the BIMI group was an outlier in number of days of in‐patient care during the postrandomisation period. When they were removed, the mean cost for the BIMI group was £16 825 (SD £12 159). The EQ5D‐5L scores were similar between the two groups.For the secondary outcome of cost‐effectiveness, service use in the period prior to baseline was relatively similar between the two groups. All participants were in‐patients during the baseline period and all but one were also in‐patients during the postrandomisation period (one participant in the BIMI group went on home leave on the day of randomisation). Although we do not test for differences for individual services, it was observed that during the postrandomisation period there was a greater number of participants in contact with psychiatrists in the BIMI group (BIMI n = 7) and five individual interviews were conducted with staff who delivered the BIMI. Twenty‐one participants who received the intervention were interviewed at follow‐up. Qualitative data revealed that participants and staff found the BIMI both feasible and acceptable when delivered as part of routine care on in‐patient wards. Whilst most participants reported on positive non‐specific factors of the intervention, such as ‘staff giving time’ and ‘going out of their way’, several participants identified that the intervention allowed them to recognise the amount of substances they were using, the pros and cons/effects of their use on them or their mental health, as well as developing new coping strategies and techniques. For in‐patient staff and the specialist practitioners, the targeted, motivational style of the intervention and the brevity of the sessions were found to be useful to engage patients in discussions about their substance use. There were mixed views about the timing of the intervention and how well it worked when in‐patients were acutely unwell; other issues included the practicalities of joint working between in‐patient and community‐based staff; and conflict with duties on the ward.Two focus groups  and Professor Paul McCrone Health Service and Population Research Department, Institute of Psychiatry, Kings College London (paul.mccrone@kcl.ac.uk). Trial Registration: NIHR CRN/UKCRN:13978 date of registration – 06.02.13; ISRCTN43548483 date of registration – 4/17/2014.The authors’ declare that they have no competing interests. All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare that (i)  have support for the submitted work; (ii)  have no relationships with any companies that might have an interest in the submitted work in the previous 3 years; (iii) their spouses, partners or children have no financial relationships that may be relevant to the submitted work; and (iv)  have no non‐financial interests that may be relevant to the submitted work. The statistical analyses were performed by Professor Nick Freemantle Department of Primary Care and Population Health, UCL Medical School (