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Blockchain and clinical trial : securing patient data
\"This book aims to highlight the gaps and the transparency issues in the clinical research and trials processes and how there is a lack of information flowing back to researchers and patients involved in those trials. Lack of data transparency is an underlying theme within the clinical research world and causes issues of corruption, fraud, errors and a problem of reproducibility. Blockchain can prove to be a method to ensure a much more joined up and integrated approach to data sharing and improving patient outcomes. Surveys undertaken by creditable organisations in the healthcare industry are analysed in this book that show strong support for using blockchain technology regarding strengthening data security, interoperability and a range of beneficial use cases where mostly all respondents of the surveys believe blockchain will be important for the future of the healthcare industry. Another aspect considered in the book is the coming surge of healthcare wearables using Internet of Things (IoT) and the prediction that the current capacity of centralised networks will not cope with the demands of data storage. The benefits are great for clinical research, but will add more pressure to the transparency of clinical trials and how this is managed unless a secure mechanism like, blockchain is used\"--Publisher's description.
Book
Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database
Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the ‘Orphanet Epidemiological file’ (http://www.orphadata.org/cgi-bin/epidemio.html). Of the 5304 diseases defined by point prevalence, 84.5% of those analysed have a point prevalence of <1/1 000 000. However 77.3–80.7% of the population burden of rare diseases is attributable to the 4.2% (n = 149) diseases in the most common prevalence range (1–5 per 10 000). Consequently national definitions of ‘Rare Diseases’ (ranging from prevalence of 5 to 80 per 100 000) represent a variable number of rare disease patients despite sharing the majority of rare disease in their scope. Our analysis yields a conservative, evidence-based estimate for the population prevalence of rare diseases of 3.5–5.9%, which equates to 263–446 million persons affected globally at any point in time. This figure is derived from data from 67.6% of the prevalent rare diseases; using the European definition of 5 per 10 000; and excluding rare cancers, infectious diseases, and poisonings. Future registry research and the implementation of rare disease codification in healthcare systems will further refine the estimates.
Journal Article
Towards a standardized bioinformatics infrastructure for N- and O-glycomics
The mass spectrometry (MS)-based analysis of free polysaccharides and glycans released from proteins, lipids and proteoglycans increasingly relies on databases and software. Here, we review progress in the bioinformatics analysis of protein-released
N
- and
O
-linked glycans (
N
- and
O
-glycomics) and propose an e-infrastructure to overcome current deficits in data and experimental transparency. This workflow enables the standardized submission of MS-based glycomics information into the public repository UniCarb-DR. It implements the MIRAGE (Minimum Requirement for A Glycomics Experiment) reporting guidelines, storage of unprocessed MS data in the GlycoPOST repository and glycan structure registration using the GlyTouCan registry, thereby supporting the development and extension of a glycan structure knowledgebase.
Glycomics is gaining momentum in basic, translational and clinical research. Here, the authors review current reporting standards and analysis tools for mass-spectrometry-based glycomics, and propose an e-infrastructure for standardized reporting and online deposition of glycomics data.
Journal Article
Preprints: An underutilized mechanism to accelerate outbreak science
In an Essay, Michael Johansson and colleagues advocate the posting of research studies addressing infectious disease outbreaks as preprints.In an Essay, Michael Johansson and colleagues advocate the posting of research studies addressing infectious disease outbreaks as preprints.
Journal Article
Tradeoffs between accuracy measures for electronic health care data algorithms
We review the uses of electronic health care data algorithms, measures of their accuracy, and reasons for prioritizing one measure of accuracy over another.
We use real studies to illustrate the variety of uses of automated health care data in epidemiologic and health services research. Hypothetical examples show the impact of different types of misclassification when algorithms are used to ascertain exposure and outcome.
High algorithm sensitivity is important for reducing the costs and burdens associated with the use of a more accurate measurement tool, for enhancing study inclusiveness, and for ascertaining common exposures. High specificity is important for classifying outcomes. High positive predictive value is important for identifying a cohort of persons with a condition of interest but that need not be representative of or include everyone with that condition. Finally, a high negative predictive value is important for reducing the likelihood that study subjects have an exclusionary condition.
Epidemiologists must often prioritize one measure of accuracy over another when generating an algorithm for use in their study. We recommend researchers publish all tested algorithms—including those without acceptable accuracy levels—to help future studies refine and apply algorithms that are well suited to their objectives.
Journal Article
The drug effects questionnaire: psychometric support across three drug types
Rationale
The Drug Effects Questionnaire (DEQ) is widely used in studies of acute subjective response (SR) to a variety of substances, but the format of the DEQ varies widely across studies, and details of its psychometric properties are lacking. Thus, the field would benefit from demonstrating the reliability and validity of the DEQ for use across multiple substances.
Objective
The current study evaluated the psychometric properties of several variations of DEQ items, which assessed the extent to which participants (1) feel any substance effect(s), (2) feel high, (3) like the effects, (4) dislike the effects, and (5) want more of the substance using 100-mm visual analog scales.
Methods
DEQ data from three placebo-controlled studies were analyzed to examine SR to amphetamine, nicotine, and alcohol. We evaluated the internal structure of the DEQ for use with each substance as well as relationships between scale items, measures of similar constructs, and substance-related behaviors.
Results
Results provided preliminary psychometric support for items assessing each DEQ construct (feel, high, dislike, like, and more).
Conclusions
Based on the study results, we identify several common limitations of extant variants of the DEQ and recommend an improved version of the measure. The simplicity and brevity of the DEQ combined with its promising psychometric properties support its use in future SR research across a variety of substances.
Journal Article
Perspective: Sustaining the big-data ecosystem
Organizing and accessing biomedical big data will require quite different business models, say Philip E. Bourne, Jon R. Lorsch and Eric D. Green.
Journal Article
Comparative risk of malignancies and infections in patients with rheumatoid arthritis initiating abatacept versus other biologics: a multi-database real-world study
Background
Patients with rheumatoid arthritis (RA) are at an increased risk of developing certain cancers and infections compared with the general population. Biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) are effective treatment options for RA, but limited evidence is available on the comparative risks among b/tsDMARDs. We assessed the risk of malignancies and infections in patients with RA who initiated abatacept versus other b/tsDMARDs in a real-world setting.
Methods
This retrospective, observational study used administrative data from three large US healthcare databases (MarketScan, PharMetrics, and Optum) to identify patients treated with abatacept or other b/tsDMARDs. In both groups, age-stratified incidence rates (IRs) with 95% confidence intervals (CIs) were calculated for total malignancy and hospitalized infections; propensity score matching and Cox proportional hazards regression models were used to estimate hazard ratios (HRs) with 95% CIs for total malignancy, lung cancer, lymphoma, breast cancer, non-melanoma skin cancer (NMSC), hospitalized infections, opportunistic infections, and tuberculosis (TB), both within individual databases and in meta-analyses across the three databases.
Results
A rounded total of 19.2, 13.6, and 4.2 thousand patients initiating abatacept and 55.3, 40.8, and 13.8 thousand initiating other b/tsDMARDs were identified in the MarketScan, PharMetrics, and Optum databases, respectively. The IRs for total malignancy and hospitalized infections were similar between the two groups in each age stratum. In meta-analyses, total malignancy risk (HR [95% CI] 1.09 [1.02–1.16]) of abatacept versus other b/tsDMARDs was slightly but statistically significantly increased; small, but not statistically significant, increases were seen for lung cancer (1.10 [0.62–1.96]), lymphoma (1.27 [0.94–1.72]), breast cancer (1.15 [0.92–1.45]), and NMSC (1.10 [0.93–1.30]). No significant increase in hospitalized infections (0.96 [0.84–1.09]) or opportunistic infections (1.06 [0.96–1.17]) was seen. For TB, low event counts precluded meta-analysis.
Conclusions
In this real-world multi-database study, the risks for specific cancers and infections did not differ significantly between patients in the abatacept and other b/tsDMARDs groups. The slight increase in total malignancy risk associated with abatacept needs further investigation. These results are consistent with the established safety profile of abatacept.
Journal Article
Data sharing: Empty archives
Most researchers agree that open access to data is the scientific ideal, so what is stopping it happening? Bryn Nelson investigates why many researchers choose not to share.
Journal Article