39 research outputs found

    Accuracy of the electronic patient record in a first opinion veterinary practice

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    The use of electronic patient records (EPRs) in veterinary research is becoming more common place. To date no-one has investigated how accurately and completely they represent the clinical interactions that happen between veterinary professionals, and their clients and patients. The aim of this study was to compare data extracted from consultations within EPRs with data gathered by direct observation of the same consultation. A secondary aim was to establish the inter-rater reliability of two researchers who examined the data extracted from the EPRs. A convenience sample of 36 small animal consultations undertaken by 2 veterinary surgeons (83% by one veterinary surgeon) at a mixed veterinary practice in the United Kingdom was studied. All 36 consultations were observed by a single researcher using a standardised data collection tool. The information recorded in the EPRs was extracted from the Practice Management Software (PMS) systems using a validated XML schema. The XML extracted data was then converted into the same format as the observed data by two independent researchers who examined the extracted information and recorded their findings using the same tool as for the observation. The issues discussed and any action taken relating to those problems recorded in the observed and extracted datasets were then compared. In addition the inter-rater reliability of the two researchers who examined the extracted data was assessed. Only 64.4% of the observed problems discussed during the consultations were recorded in the EPR. The type of problem, who raised the problem and at what point in the consultation the problem was raised significantly affected whether the problem was recorded or not in the EPR. Only 58.3% of observed actions taken during the consultations were recorded in the EPR and the type of action significantly affected whether it would be recorded or not. There was moderate agreement between the two researchers who examined the extracted data. This is the first study that examines how much of the activity that occurs in small animal consultations is recorded in the EPR. Understanding the completeness, reliability and validity of EPRs is vital if they are to continue to be used for clinical research and the results to direct clinical care

    Corporate philanthropy, political influence, and health policy

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    Background The Framework Convention of Tobacco Control (FCTC) provides a basis for nation states to limit the political effects of tobacco industry philanthropy, yet progress in this area is limited. This paper aims to integrate the findings of previous studies on tobacco industry philanthropy with a new analysis of British American Tobacco's (BAT) record of charitable giving to develop a general model of corporate political philanthropy that can be used to facilitate implementation of the FCTC. Method Analysis of previously confidential industry documents, BAT social and stakeholder dialogue reports, and existing tobacco industry document studies on philanthropy. Results The analysis identified six broad ways in which tobacco companies have used philanthropy politically: developing constituencies to build support for policy positions and generate third party advocacy; weakening opposing political constituencies; facilitating access and building relationships with policymakers; creating direct leverage with policymakers by providing financial subsidies to specific projects; enhancing the donor's status as a source of credible information; and shaping the tobacco control agenda by shifting thinking on the importance of regulating the market environment for tobacco and the relative risks of smoking for population health. Contemporary BAT social and stakeholder reports contain numerous examples of charitable donations that are likely to be designed to shape the tobacco control agenda, secure access and build constituencies. Conclusions and Recommendations Tobacco companies' political use of charitable donations underlines the need for tobacco industry philanthropy to be restricted via full implementation of Articles 5.3 and 13 of the FCTC. The model of tobacco industry philanthropy developed in this study can be used by public health advocates to press for implementation of the FCTC and provides a basis for analysing the political effects of charitable giving in other industry sectors which have an impact on public health such as alcohol and food

    Identification of pediatric septic shock subclasses based on genome-wide expression profiling

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    <p>Abstract</p> <p>Background</p> <p>Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling.</p> <p>Methods</p> <p>Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization.</p> <p>Results</p> <p>Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the three putative subclasses (analysis of variance, Bonferonni correction, <it>P </it>< 0.05) identified 6,934 differentially regulated genes. K-means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the three subclasses. Leave one out cross-validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C.</p> <p>Conclusion</p> <p>Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.</p

    Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

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    Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

    2022 World Hypertension League, Resolve To Save Lives and International Society of Hypertension dietary sodium (salt) global call to action

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    Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015–2020

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    Background Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19. Methods and findings We used data from the World Health Organization’s Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015–2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity. Conclusions Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020. Natalie L Cobb and colleagues use routine influenza surveillance data to detect outliers in influenza-like-illness during the COVID-19 pandemic. Author summary Why was this study done? Early detection of respiratory viral outbreaks, such as SARS-CoV-2, is key for public health response and mitigation measures. In this study, we used routine influenza surveillance data to detect outliers in influenza-like illness (ILI) during the COVID-19 pandemic that could suggest spread of SARS-CoV-2. We hypothesized that using data-driven methods would identify increased case counts of influenza-negative ILI prior to reported peaks of COVID-19. What did the researchers do and find? We used routine influenza surveillance data from the World Health Organization’s FluNet and applied automated outlier detection methods to identify outliers in influenza-negative ILI in 2020 across 28 countries. In 16 countries, we detected outliers that preceded the first reported COVID-19 peaks, with an average lag time of 13.3 weeks. In 7 countries, the week of the first outlier changed when accounting for missing data in the models. What do these findings mean? This study serves as a proof of concept and suggests a potential role for the use of automated data monitoring and outlier detection systems to identify outbreaks in respiratory viral illness. These findings also highlight the importance of strengthening routine disease surveillance networks to enhance our ability to identify novel diseases and inform public health responses on a global scale
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