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Classical stewardship efforts have targeted immunocompetent patients; however, appropriate use of antimicrobials in the immunocompromised host has become a target of interest. Cytomegalovirus (CMV) infection is one of the most common and significant complications after solid-organ transplant (SOT). The treatment of CMV requires a dual approach of antiviral drug therapy and reduction of immunosuppression for optimal outcomes. This dual approach to CMV management increases complexity and requires individualization of therapy to balance antiviral efficacy with the risk of allograft rejection. In this review, we focus on the development and implementation of CMV stewardship initiatives, as a component of antimicrobial stewardship in the immunocompromised host, to optimize the management of prevention and treatment of CMV in SOT recipients. These initiatives have the potential not only to improve judicious use of antivirals and prevent resistance but also to improve patient and graft survival given the interconnection between CMV infection and allograft function.
Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.
We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.
16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).
PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
The calibration hardware system of the Large Synoptic Survey Telescope (LSST) is designed to measure two quantities: a telescope’s instrumental response and atmospheric transmission, both as a function of wavelength. First of all, a “collimated beam projector” is designed to measure the instrumental response function by projecting monochromatic light through a mask and a collimating optic onto the telescope. During the measurement, the light level is monitored with a NIST-traceable photodiode. This method does not suffer from stray light effects or the reflections (known as ghosting) present when using a flat-field screen illumination, which has a systematic source of uncertainty from uncontrolled reflections. It allows for an independent measurement of the throughput of the telescope’s optical train as well as each filter’s transmission as a function of position on the primary mirror. Second, CALSPEC stars can be used as calibrated light sources to illuminate the atmosphere and measure its transmission. To measure the atmosphere’s transfer function, we use the telescope’s imager with a Ronchi grating in place of a filter to configure it as a low resolution slitless spectrograph. In this paper, we describe this calibration strategy, focusing on results from a prototype system at the Cerro Tololo Inter-American Observatory (CTIO) 0.9 meter telescope. We compare the instrumental throughput measurements to nominal values measured using a laboratory spectrophotometer, and we describe measurements of the atmosphere made via CALSPEC standard stars during the same run.
Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.
To evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.
Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).
The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
Declaration of interest
Drs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Mental disorders in children and adolescents have an impact on educational attainment.
To examine the temporal association between attainment in education and subsequent diagnosis of depression or self-harm in the teenage years.
General practitioner, hospital and education records of young people in Wales between 1999 and 2014 were linked and analysed using Cox regression.
Linked records were available for 652 903 young people and of these 33 498 (5.1%) developed depression and 15 946 (2.4%) self-harmed after the age of 12 but before the age of 20. Young people who developed depression over the study period were more likely to have achieved key stage 1 (age 7 years) but not key stage 2 (age 11) (hazard ratio (HR) = 0.79, 95% CI 0.74–0.84) milestones, indicating that they were declining in academic attainment during primary school. Conversely, those who self-harmed were achieving as well as those who did not self-harm in primary school, but showed a severe decline in their attainment during secondary school (HR = 0.72, 95% CI 0.68–0.78).
Long-term declining educational attainment in primary and secondary school was associated with development of depression in the teenage years. Self-harm was associated with declining educational attainment during secondary school only. Incorporating information on academic decline with other known risk factors for depression/self-harm (for example stressful life events, parental mental health problems) may improve risk profiling methods.
In this essay we consider how the principles evident in Dewey's Democracy and Education would have been evident in response to the civil rights movement that took shape shortly after his death, and to the major educational reform movements of today. While acknowledging that Dewey's views on race and human development were inevitably influenced by his social and intellectual context, we maintain that he was fundamentally opposed to racist ideology and related popular beliefs, and that his deep commitment to democracy as a social process would have made him a staunch supporter of the civil rights movement and associated demands for racial equality. We likewise argue that Dewey would have had deep misgivings about the standards-driven assessment regimes that underlie current national reform efforts. In the end we suggest that Democracy and Education still has much to offer students of education today, and can serve as a helpful guide to those who would seek to change educational practice for the better.
Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity.
To confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis.
The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT.
In the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β=0.12, P = 2.7 × 10−4) and with the Han/Eskin random effects method (P = 1.4 × 10−7) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10−8). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTO.
This meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression.
This is not the first book written about data librarianship, and hopefully it will not be the last, but it is one of very few, all written within the past few years, that reflects the growing interest in research data support. Academic data librarians help staff and students with all aspects of this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes, to create new knowledge.
Our aim is to offer an insider's view of data librarianship as it is today, with plenty of practical examples and advice. At times we try to link this to wider academic research agendas and scholarly communication trends past, present and future, while grounding these thoughts back in the everyday work of data librarians and other information professionals.
We would like to tell you a little bit about ourselves as the authors, but first a word about you. We have two primary groups of readers in mind for this book: library and iSchool students and their teachers, and working professionals (especially librarians) learning to deal with data. We would be honoured to have this book used as an educational resource in library and information graduate programmes, because we believe the future of data librarianship (regardless of its origins, examined in Chapter 1) lies with academic libraries, and for that to become a stronger reality it needs to be studied as a professional and academic subject. To aid the use of this book as a text for study we have provided ‘key take-away points’ and ‘reflective questions’ at the end of each chapter. These can be used by teachers for individual or group assignments, or by individuals to self-assess and reinforce what they may have learned from reading each chapter.
Equally important, we empathetically address the librarian, academic, or other working expert who feels their working life is pulling them towards data support or that area of academic activity known as research data management (RDM).
What is meant by confidential or sensitive data? In Chapter 2 we outlined some of the formats of common types of research data. The field is of course wide and may include datasets that are based on numbers, text or audiovisual material. Most of the time researchers will be interested in how they can manipulate these in order to address the goals of their investigations. Some researchers are interested less in the format than in what they consider to be fundamental problems of confidentiality or data sensitivity. They have concerns about managing the data securely while they are working with it – and are unsure what they are supposed to do. Others have concerns about the appropriateness of allowing preservation or re-use of the same material. Understanding what a researcher actually means when they state their data are confidential therefore becomes significant. This is a growing area of discussion and is demonstrated by the huge interest shown by researchers in training and awareness raising courses on this topic. Typical concerns include whether the data:
1 were collected just for a specific research project
2 are still needed for future analysis
3 may only be properly understood by the original researchers
4 should be destroyed once they have been used or analysed
5 could be exploited for non-academic purposes
6 cover a subject area that is not for public consumption such as studies of terrorists, family history or locations of endangered species
7 contain general information that could allow participants to be Identified
8 include names and addresses or similar personal information
9 include particular details that could be harmful to participants if made Public
10 are to be treated as confidential so an unintended release in itself would be damaging.
Such a wide range of concerns illustrates that no data type is inherently more confidential than another. It is possible to encounter variations on some or all of these arguments from researchers working with statistical data, video interviews or samples of genetic material. However it is commonplace to find researchers working under the assumption that all their data are confidential and present unique problems. The role of the data librarian is to help shift that perception.
Many data librarians working today are solo librarians or part of a small team. However, we would argue that for those data librarians who have become involved in work supporting research data management (RDM), working alone is not an option. There are too many components involved in institutional RDM support for one professional to be able to do it all. One parallel trend to employing data librarians in the UK is for academic libraries to hire an ‘RDM services coordinator’. These may be the only full-time professional working on RDM, and their title recognizes the need to leverage input from service managers across the library and computing service, and beyond. In some cases these new posts have been based in the research office, rather than the library. In other cases support has been cobbled together from parts of people's jobs across libraries, research offices and IT departments. A DCC survey from 2015 found that ‘At least two-thirds of [52 responding UK-based] institutions currently have less than 1 [full-time equivalent staff] allocated to RDM’ with only those receiving the top third of research income expected to have more (almost three on average) by May 2016 (Whyte, 2015, 3).
For those without the luxury of being in a dedicated post, taking on data support is often a new job requirement from which none of the previous duties have been removed. While this may cause stress and even resentment for some, it really should be seen as an intellectual challenge and an opportunity to work with new researchers and colleagues outside one's normal circles. Even those in a dedicated data-related post who may be accustomed to serving a specific research community are being challenged to come out of their silos and combine forces with others across campus to join RDM committees and do some collective problem-solving. People skills or communication skills have never been more needed in the data support profession.
Similarly, it may be observed that there are two types of research institutions: those that embrace the RDM challenge proactively, and those that wait for funders’ requirements to threaten research income or other external consequences (such as a scandal involving fraud, confidentiality breach or failure to comply with a freedom of information [FOI] request) before reluctantly taking action.
A task faced by many data librarians is the acquisition and development of digital resources in the context of a larger library of printed material. This can be done through adoption of a range of procedures but there are also benefits in having a formal written policy. A formal description acknowledges that a policy fulfils many functions beyond being merely a tool for choosing materials. In addition to describing current collections, it encourages ‘the staff involved to (re)consider the aims and objectives of the organization, both long and short term, and the priorities to be attached to different activities. It assists with budgeting, serves as communication channel within a library and between the library and outside constituents, supports co-operative collection development, prevents censorship, and assists in overall collection management activities’ (IFLA, 2001).
Development of a formal policy is recommended and it ought to encapsulate the range of activities to be undertaken by the data librarian. It will involve selection of materials of course but will equally build on the relationships with readers within a department and library, mechanisms for promoting resources and receiving feedback, and raising the profile of your work within the larger organization. The policy needs to acknowledge that collection development can be different where data are concerned.
Data as a resource to be acquired
What are the various issues to consider? Many traditional topics to do with library print collections apply equally to those of us who are building up digital research data collections. Some institutions may regard the latter as more the responsibility of IT services or archival holdings if a digitization project has been involved, but the continuing evolution of academic libraries makes it important to stake a claim for responsibility in this area. In Data Basics, a seminal monograph on developing data libraries and support services, Geraci, Humphrey and Jacobs argue, ‘The role of the library is to Select, Acquire, Organize, and Preserve information, and to provide Access to and Services for that information. Although some librarians question these roles in the digital world … these are the activities that define a library. If an organization fulfils all these roles … what would we call it but “a library”?’ (Geraci, Humphrey and Jacobs, 2012, 65).
The daily work of the data librarian can be quite similar to that of our more traditional named academic librarians. It can involve working within library systems, acquiring resources and developing working relationships that allow you to promote the role of your library. The fact we work with research data alongside periodicals, books and other publications should not make that much of a difference to how our work is seen but for a variety of reasons it does. The word ‘data’ itself is off-putting to some traditional academic librarians and researchers and a cause of some anxiety. For some it is because it seems to belong to other disciplines and have little relationship with their own work. Others see it as being such a common word as to be almost indistinguishable from ‘information’.
A good example of these different perspectives can be seen in the social sciences. Researchers that use survey techniques and questionnaires create a body of data that can be recorded, analysed, summarized in the form of statistics and, in due course, form the basis of publications. They are clear that this is their dataset and appreciate its role in enabling analysis. It is numerical, quantitative and quite obviously ‘data’. On the other hand researchers using qualitative techniques such as semi-structured interviews or focus groups create a body of work that includes audio transcriptions as well as perhaps audio recordings or photographs. This material is just as crucial in informing analysis but within that tradition it is routinely not perceived or referred to as ‘data’. A similar situation can be seen in many disciplines within the humanities (Ward et al., 2011), which focus on working with ‘primary materials’. Within visual art research a similar resistance has been noted so that there is, ‘An additional task to those working towards the same ends in other disciplines: the translation of scientific RDM concepts into language meaningful to those working in creative arts disciplines’ (Guy, Donnelly and Molloy, 2013, 101).
Data librarians need to be aware of these nuances if they are to be successful in promoting RDM. Reflection on the attitudes you encounter and adopting the terminology of different user communities will be important in offering effective support from your library during a research project.
Academic librarians play an important role in supporting the research process throughout its many phases. In order to maintain this level of support they also need to be responsive to new requirements being placed on researchers. So while there are expectations from institutions and funders that more thought be given to the general principles of data management, there are also specific stages in the data lifecycle where new and potentially unfamiliar activities may be identified and supported. The production of a DMP is a perfect example of such an activity. It is an important stage in the management of research data where, ‘Principal investigators (PIs) … document their plans for describing, storing, securing, sharing, and preserving their research data’ (Bishoff and Johnston, 2015, 1), but also presents an opportunity for academic librarians to give guidance and introduce themselves to their target audience. Preparation of a DMP benefits from various stages of redrafting much like a consent agreement (a document discussed within this chapter and in more detail in Chapter 8). It is an opportunity for the data librarian to offer support and advice for what is becoming a key document – but also one that may be unfamiliar even to experienced researchers – which will have a great deal of impact.
Leading by example: eight vignettes
In this chapter we have selected case studies – or vignettes – from a range of disciplines to show how the demands of RDM are being dealt with at different institutions and how advice on data management planning is being used to establish links and recognition. Each is written by a different data librarian, research data manager or data professional, and illustrates how the needs of researchers are addressed through a mixture of awareness raising about RDM and troubleshooting specific details of project administration.
Social science research at the London School of Economics and Political Science
Laurence Horton, Data Librarian, Digital Library, London School of Economics
Before the LSE Library set up an RDM support service in 2014, data management planning support consisted of the research office providing award applicants with a plan from someone's previously successful application and letting them copy and adapt it.
A university has been defined as ‘just a group of buildings gathered around a library’ (https://en.wikiquote.org/wiki/Shelby_Foote); in any case, the role of the library in academic life is a central one. Those working within libraries make a valuable contribution to supporting research and teaching as well as shaping the character and intellectual life of individual institutions. Whether a university focuses on the humanities, physical sciences, classics or any other number of disciplines, the librarian ultimately works to support learning and the spread of knowledge. This may take many established forms but increasingly there is a need to support new forms of information. Digital data is one particular new form. In the case of data collections and research data creation this has also led to the rise of a new kind of library professional: the data librarian. But to what extent is this in fact a new role and in what ways does it differ from traditional librarianship?
For example, one role of the librarian is to deal with what may be called the lifecycle of information resources. These are the varied tasks to do with evaluation, selection, purchasing and promotion, and preservation of materials within the library. This relies on having a good working knowledge of what readers in a particular area need for their work. It also draws on a familiarity with what is being made available by publishers and other suppliers of information resources. The terms employed to describe a researcher also indicate the orientation or origin of research support services. Some may prefer traditional terms such as patron or – as favoured at the University of Oxford – reader, since this gives continuity to existing provision. The medium or methodologies being applied to the data are unimportant. On the other hand those working on support services created specifically to deal with digital data may feel older terms are inappropriate or anachronistic. Since digital information is often used in conjunction with software it is no longer ‘human-readable’ at all and its value lies in the fact it can be easily supplied to researchers. Their role is to manipulate, interpret, analyse, watch, listen to, or more generally ‘use’ the data. For this reason data centres or repositories often refer to ‘users’ of data.