Understanding barriers and facilitators to palliative and end-of-life care research: a mixed method study of generalist and specialist health, social care, and research professionals.

BACKGROUND: Palliative care provision should be driven by high quality research evidence. However, there are barriers to conducting research. Most research attention focuses on potential patient barriers; staff and organisational issues that affect research involvement are underexplored. The aim of this research is to understand professional and organisational facilitators and barriers to conducting palliative care research. METHODS: A mixed methods study, using an open cross-sectional online survey, followed by working groups using nominal group techniques. Participants were professionals interested in palliative care research, working as generalist/specialist palliative care providers, or palliative care research staff across areas of North West England. Recruitment was via local health organisations, personal networks, and social media in 2022. Data were examined using descriptive statistics and content analysis. RESULTS: Participants (survey n = 293, working groups n = 20) were mainly from clinical settings (71%) with 45% nurses and 45% working more than 10 years in palliative care. 75% were not active in research but 73% indicated a desire to increase research involvement. Key barriers included lack of organisational research culture and capacity (including prioritisation and available time); research knowledge (including skills/expertise and funding opportunities); research infrastructure (including collaborative opportunities across multiple organisations and governance challenges); and patient and public perceptions of research (including vulnerabilities and burdens). Key facilitators included dedicated research staff, and active research groups, collaborations, and networking opportunities. CONCLUSIONS: Professionals working in palliative care are keen to be research active, but lack time, skills, and support to build research capabilities and collaborations. A shift in organisational culture is needed to enhance palliative care research capacity and collaborative opportunities across clinical and research settings.

Beliefs about weight and breast cancer: an interview study with high risk women following a 12 month weight loss intervention.

BACKGROUND: Breast cancer is the most common cancer in the UK. Lifestyle factors including excess weight contribute to risk of developing the disease. Whilst the exact links between weight and breast cancer are still emerging, it is imperative to explore how women understand these links and if these beliefs impact on successful behaviour change. METHOD: Overweight/obese premenopausal women (aged 35-45) with a family history of breast cancer (lifetime risk 17-40%) were invited to a semi-structured interview following their participation in a 12 month weight loss intervention aimed at reducing their risk of breast cancer. Interviews were carried out with 9 women who successfully achieved ≥5% weight loss and 11 who were unsuccessful. Data were transcribed verbatim and analysed using thematic analysis. RESULTS: Three themes were developed from the analysis. The first theme how women construct and understand links between weight and breast cancer risk is composed of two subthemes, the construction of weight and breast cancer risk and making sense of weight and breast cancer risk. This theme explores women's understanding of what contributes to breast cancer risk and whether they believe that weight loss could reduce their breast cancer risk. The second theme motivation and adherence to weight loss interventions explains that breast cancer risk can be a motivating factor for adherence to a weight loss intervention. The final theme, acceptance of personal responsibility for health is composed of two subthemes responsibility for one's own health and responsibility for family health through making sensible lifestyle choices. CONCLUSION: Beliefs about weight and breast cancer risk were informed by social networks, media reports and personal experiences of significant others diagnosed with breast cancer. Our study has highlighted common doubts, anxieties and questions and the importance of providing a credible rationale for weight control and weight loss which addresses individual concerns. Counselling and health education material should be tailored to facilitate understanding of both genetic and modifiable risk factors and should do more help individuals to visualise the weight and breast cancer link.

The role of relationship attachment in psychological adjustment to cancer in patients and caregivers: a systematic review of the literature.

OBJECTIVE: The objective of this works is to report the results of a systematic review to evaluate the role of attachment in adjustment to cancer for patients and those close to them. METHODS: A systematic search of electronic databases was undertaken, identifying literature published up to June 2013. PsychINFO, Medline and the Cumulative Index to Nursing & Allied Health Literature were searched using search strings related to cancer, relationships, attachment and commonly assessed self-report psychosocial outcome measures. Extracted papers were assessed for their relevance. Key data were extracted to spreadsheets, and two raters coded the quality of the research. RESULTS: Following inclusion assessment, data were extracted from 15 quantitative studies. Scores from patients or caregivers on attachment questionnaires did not differ greatly from normative data. A more insecure attachment style has poorer outcomes for patients in terms of their psychological adjustment to cancer and their ability to perceive and access social support. A secure attachment style is associated with positive growth and better well-being. A more insecure attachment style in caregivers was associated with depression, higher caregiving stress, less autonomous motivations for caregiving and difficulties with caregiving. CONCLUSIONS: An awareness of attachment theory and the ways in which different forms of insecure attachment impact on patients and caregivers and their well-being may substantially improve the ability of those working with cancer patients and their families to better understand and provide for their support needs. The development and evaluation of support interventions tailored to different attachment styles remains a longer-term goal.

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer.

INTRODUCTION: Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. METHODS: More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. RESULTS: The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working. CONCLUSIONS: With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.

Building the observational medical outcomes partnership's T-MSIS Analytic File common data model.

Objectives: This effort used Databricks to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methods: Our process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. Results: The final CDM contained 119,048,562 individuals and 24,806,828,121 clinical observations from 2014 through 2018. Discussion: The transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. Conclusion: Our effort successfully used Databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies.

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States.

BACKGROUND: Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance. OBJECTIVES: This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series. METHODS: Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series. RESULTS: Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement). CONCLUSION: Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

Development of an exposure measurement database on five lung carcinogens (ExpoSYN) for quantitative retrospective occupational exposure assessment.

BACKGROUND: SYNERGY is a large pooled analysis of case-control studies on the joint effects of occupational carcinogens and smoking in the development of lung cancer. A quantitative job-exposure matrix (JEM) will be developed to assign exposures to five major lung carcinogens [asbestos, chromium, nickel, polycyclic aromatic hydrocarbons (PAH), and respirable crystalline silica (RCS)]. We assembled an exposure database, called ExpoSYN, to enable such a quantitative exposure assessment. METHODS: Existing exposure databases were identified and European and Canadian research institutes were approached to identify pertinent exposure measurement data. Results of individual air measurements were entered anonymized according to a standardized protocol. RESULTS: The ExpoSYN database currently includes 356 551 measurements from 19 countries. In total, 140 666 personal and 215 885 stationary data points were available. Measurements were distributed over the five agents as follows: RCS (42%), asbestos (20%), chromium (16%), nickel (15%), and PAH (7%). The measurement data cover the time period from 1951 to present. However, only a small portion of measurements (1.4%) were performed prior to 1975. The major contributing countries for personal measurements were Germany (32%), UK (22%), France (14%), and Norway and Canada (both 11%). CONCLUSIONS: ExpoSYN is a unique occupational exposure database with measurements from 18 European countries and Canada covering a time period of >50 years. This database will be used to develop a country-, job-, and time period-specific quantitative JEM. This JEM will enable data-driven quantitative exposure assessment in a multinational pooled analysis of community-based lung cancer case-control studies.

Considering non-hospital data in clinical informatics use cases, a review of the National Emergency Medical Services Information System (NEMSIS).

Background: The National Emergency Medical Services (EMS) Information System (NEMSIS) Technical Assistance Center (TAC) collects and curates EMS activation level records for the United States. Originated as an outcomes assessment and service comparison tool, NEMSIS may have other high value clinical and public health uses. Methods: This study acquired a 100% activation level public dataset for 2019 from NEMSIS TAC and assessed item response quantities. Subsumption of NEMSIS terms within other controlled clinical vocabularies was also considered. Results: None of the assessed terminologies (LOINC, ICD10-CM, SNOMED-CT) could describe meaningful volumes of NEMSIS item response codes. The 2019 activation year dataset included 36,525 non-date/time or calculated distinct item responses for 43 activation descriptive items. Said item responses yielded 2,101,844,053 activation distinct non-blank responses. Several NEMSIS item responses had high clinical and public health value. Conclusions: NEMSIS can support multiple public health use cases in addition to EMS outcomes assessment. A comprehensive custom value set is appropriate to integrate NEMSIS item response codes into controlled terminologies, FHIR or hospital Electronic Health Record applications.

CONSIDER Statement: Consolidated Recommendations for Sharing Individual Participant Data from Human Clinical Studies.

Many research sponsors require sharing of data from human clinical trials. We created the CONSIDER statement, a set of recommendations to improve data sharing practices and increase the availability and re-usability of individual participant data from clinical trials. We developed the recommendations by reviewing shared individual participant data and study artifacts from a set of completed studies, as well as study data deposited on ClinicalTrials.gov and on several data sharing platforms. The CONSIDER statement is comprised of seven sections including: format, data sharing, study design, case report forms, data dictionary, data de-identification and choice of data sharing platform. We developed several different forms of CONSIDER which includes a brief form (the checklist), a full form (detailed descriptions and examples), and a scoring methodology. The checklist can be used to evaluate adherence to various progressive data sharing recommendations. We are currently in Phase 2 of collecting feedback on the CONSIDER statement.

Data Characterization of Medicaid: Legacy and New Data Formats in the CMS Virtual Research Data Center.

Medicaid is a significant health insurance plan providing healthcare coverage to up to a third of the population of the United Sates. We describe two different formats of Medicaid data within Center for Medicare and Medicaid Services Virtual Research Data Center. We analyze record length, age and enrollment justification among patients for both data formats. As of December 2016, the total size of Medicaid population available from CMS is 92,953,389; 45% of patients are aged 0 to 18, 26.6% are aged 19-35 and 23.2% are aged 36-64. In terms of Medicaid eligibility, 35.6% qualify due to (child) age and 26.8% qualify due to income. We also compare the volume of Medicaid to Medicare for year 2016. We conclude that Medicaid data includes patients with significant record lengths and relatively well documented enrollment justification, which are high value assets for data reuse researchers that are willing to balance known data limitations with careful analysis design and interpretation.

Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications.

BACKGROUND: With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. OBJECTIVE: In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. METHODS: We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis-specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). RESULTS: A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. CONCLUSION: We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

The changing patterns of comorbidities associated with human immunodeficiency virus infection, a longitudinal retrospective cohort study of Medicare patients.

ABSTRACT: The objective of this paper is to determine the temporal trend of the association of 66 comorbidities with human immunodeficiency virus (HIV) infection status among Medicare beneficiaries from 2000 through 2016.We harvested patient level encounter claims from a 17-year long 100% sample of Medicare records. We used the chronic conditions warehouse comorbidity flags to determine HIV infection status and presence of comorbidities. We prepared 1 data set per year for analysis. Our 17 study data sets are retrospective annualized patient level case histories where the comorbidity status reflects if the patient has ever met the comorbidity case definition from the start of the study to the analysis year.We implemented one logistic binary regression model per study year to discover the maximum likelihood estimate (MLE) of a comorbidity belonging to our binary classes of HIV+ or HIV- study populations. We report MLE and odds ratios by comorbidity and year.Of the 66 assessed comorbidities, 35 remained associated with HIV- across all model years, 19 remained associated with HIV+ across all model years. Three comorbidities changed association from HIV+ to HIV- and 9 comorbidities changed association from HIV- to HIV+.The prevalence of comorbidities associated with HIV infection changed over time due to clinical, social, and epidemiological reasons. Comorbidity surveillance can provide important insights into the understanding and management of HIV infection and its consequences.

Regionally-structured explanations behind area-level populism: An update to recent ecological analyses.

Heavy geographic patterning to the 2016 Brexit vote in UK and Trump vote in US has resulted in numerous ecological analyses of variations in area-level voting behaviours. We extend this work by employing modelling approaches that permit regionally-specific associations between outcome and explanatory variables. We do so by generating a large number of regional models using penalised regression for variable selection and coefficient evaluation. The results reinforce those already published in that we find associations in support of a 'left-behind' reading. Multivariate models are dominated by a single variable-levels of degree-education. Net of this effect, 'secondary' variables help explain the vote, but do so differently for different regions. For Brexit, variables relating to material disadvantage, and to a lesser extent structural-economic circumstances, are more important for regions with a strong industrial history than for regions that do not share such a history. For Trump, increased material disadvantage reduces the vote both in global models and models built mostly for Southern states, thereby undermining the 'left-behind' reading. The reverse is nevertheless true for many other states, particularly those in New England and the Mid-Atlantic, where comparatively high levels of disadvantage assist the Trump vote and where model outputs are more consistent with the UK, especially so for regions with closer economic histories. This pattern of associations is exposed via our regional modelling approach, application of penalised regression and use of carefully designed visualization to reason over 100+ model outputs located within their spatial context. Our analysis, documented in an accompanying github repository, is in response to recent calls in empirical Social and Political Science for fuller exploration of subnational contexts that are often controlled out of analyses, for use of modelling techniques more robust to replication and for greater transparency in research design and methodology.

Identification of Common Data Elements from Pivotal FDA Trials.

It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.

A Descriptive Study of HIV Patients Highly Adherent to Antiretroviral.

HIV medication adherence is a topic of major public health concern in the United States. Adherent patients may be less likely to experience treatment failure, AIDS presentations and extreme medical costs. We evaluate a cohort of highly adherent Medicare beneficiaries to establish if the out of pocket costs of HIV medications are an inherent barrier to adherence. We analyzed a 100% sample of Medicare Part-D prescription medications. The drug and out ofpocket costs for HIV and non-HIV medications of highly adherent cohort were extracted and analyzed. The average gross drug cost per beneficiary was $34,029for HIV medications and $11,439for non-HIV medications. Average out of pocket costs per beneficiary was $454for HIV medications and $129 for non-HIV medications. Out of pocket costs do not reasonably appear to be a barrier to adherence for Part-D beneficiaries.

Analysis of data dictionary formats of HIV clinical trials.

BACKGROUND: Efforts to define research Common Data Elements try to harmonize data collection across clinical studies. OBJECTIVE: Our goal was to analyze the quality and usability of data dictionaries of HIV studies. METHODS: For the clinical domain of HIV, we searched data sharing platforms and acquired a set of 18 HIV related studies from which we analyzed 26 328 data elements. We identified existing standards for creating a data dictionary and reviewed their use. To facilitate aggregation across studies, we defined three types of data dictionary (data element, forms, and permissible values) and created a simple information model for each type. RESULTS: An average study had 427 data elements (ranging from 46 elements to 9 945 elements). In terms of data type, 48.6% of data elements were string, 47.8% were numeric, 3.0% were date and 0.6% were date-time. No study in our sample explicitly declared a data element as a categorical variable and rather considered them either strings or numeric. Only for 61% of studies were we able to obtain permissible values. The majority of studies used CSV files to share a data dictionary while 22% of the studies used a non-computable, PDF format. All studies grouped their data elements. The average number of groups or forms per study was 24 (ranging between 2 and 124 groups/forms). An accurate and well formatted data dictionary facilitates error-free secondary analysis and can help with data de-identification. CONCLUSION: We saw features of data dictionaries that made them difficult to use and understand. This included multiple data dictionary files or non-machine-readable documents, data elements included in data but not in the dictionary or missing data types or descriptions. Building on experience with aggregating data elements across a large set of studies, we created a set of recommendations (called CONSIDER statement) that can guide optimal data sharing of future studies.

Building the capacity for psycho-Oncology research: a survey of the research barriers and training needs within the International Psycho-Oncology Society.

BACKGROUND: The International Psycho-Oncology Society (IPOS) is a multidisciplinary professional network that aims to improve psychosocial care for individuals impacted by cancer. IPOS encourages research activity, recognizing that a high-quality evidence base is essential to provide best-practice, data-driven clinical care. This study aimed to determine the barriers to research involvement and the training needs and priorities of IPOS members, with the goal of facilitating the development of training resources tailored to the needs of IPOS members. METHODS: A link to an online, cross-sectional survey was disseminated to all registered members of IPOS via email. The online survey platform SimpleSurvey was used, and questions included demographic characteristics and items related to research interests, involvement, and training needs. High priority research training needs were identified as research tasks respondents rated as highly important, yet possessed a low perceived skill level in. RESULTS: Thirty-two percent of IPOS members (n = 142) completed the survey. Participants represented 49 countries and were at a variety of career stages. Overall, participants reported spending an average of 17.3 hours per week on research (range 0-80 hours per week), with 69% of respondents wanting to increase their research involvement. The main barriers to research participation included lack of research funding (80%) and lack of protected time (63%). IPOS members identified 5 high-priority training needs: preparing successful grant applications; preparing research budgets; community-based participatory research; working with decision makers; and finding collaborators or expert consultants. Participants suggested funding access, statistical advisors, and networking and mentorship opportunities as ways to enhance research involvement. Members preferred online training modules (39%) and mentorship programs (19%) as methods by which IPOS could provide research support. IPOS was viewed as being able to contribute to many aspects of research capacity building such as networking, training, and dissemination of research findings. CONCLUSIONS: IPOS has an important role in encouraging research capacity building among members. This survey provides an agenda for workshops and training opportunities. Mainly, for respondents it was less about training in research methods and more about training in how to prepare successful grant applications, including budgets, and receiving mentorship on this as well as having opportunities to collaborate with other researchers.

Evaluation of Research Accessibility and Data Elements of HIV Registries.

BACKGROUND: Patient registries represent a long-term data collection system that is a platform for performing multiple research studies to generate real-world evidence. Many of these registries use common data elements (CDEs) and link data from Electronic Health Records. OBJECTIVE: This study evaluated HIV registry features that contribute to the registry's usability for retrospective analysis of existing registry data or new prospective interventional studies. METHODS: We searched PubMed and ClinicalTrials.gov (CTG) to generate a list of HIV registries. We used the framework developed by the European Medical Agency (EMA) to evaluate the registries by determining the presence of key research features. These features included information about the registry, request and collaboration processes, and available data. We acquired data dictionaries and identified CDEs. RESULTS: We found 13 HIV registries that met our criteria, 11 through PubMed and 2 through CTG. The prevalence of the evaluated features ranged from all 13 (100%) having published key registry information to 0 having a research contract template. We analyzed 6 data dictionaries and identified 14 CDEs that were present in at least 4 of 6 (66.7%) registry data dictionaries. CONCLUSION: The importance of registries as platforms for research data is growing and the presence of certain features, including data dictionaries, contributes to the reuse and secondary research capabilities of a registry. We found some features such as collaboration policies were in the majority of registries while others such as, ethical support, were in a few and are more for future development.

Sharing of Individual Participant Data from Clinical Trials: General Comparison and HIV Use Case.

Sharing of individual participant data is encouraged by the International Committee of Medical Journal Editors. We analyzed clinical trial registry data from ClinicalTrials.gov (CTG) and determined the proportion of trials sharing de-identified Individual Participant Data (IPD). We looked at 3,138 medical conditions (as Medical Subject Heading terms). Overall, 10.8% of trials with first registration date after December 1, 2015 answered 'Yes' to plan to share de-identified IPD data. This sharing rate ranges between 0% (biliary tract neoplasms) to 72.2% (meningitis, meningococcal) when analyzed by disease that is focus of a study. Via a predictive model, we found that studies that deposited basic summary results data to CTG results registry, large studies and phase 3 interventional studies are most likely to declare intent to share IPD data. As part of an HIV common data element analysis project, we further compared a body of HIV trials (24% sharing rate) to other diseases.

Ultra-brief non-expert-delivered defusion and acceptance exercises for food cravings: A partial replication study.

Food cravings are a common barrier to losing weight. This article presents a randomised comparison of non-expert group-delivered ultra-brief defusion and acceptance interventions against a distraction control. A total of 63 participants were asked to carry a bag of chocolates for a week while trying to resist the temptation to eat them. A behavioural rebound measure was administered. Each intervention out-performed control in respect of consumption, but not cravings. These techniques may have a place in the clinical management of food cravings. We provide tentative evidence that the mechanism of action is through decreased reactivity to cravings, not through reduced frequency of cravings.