Re-evaluation of websites from burn centers in Austria,Germany and Switzerland

Introduction

Websites serve as information and communication platforms; hence, they are important tools for the self-promotion of hospitals. In 2010, Selig et al. evaluated the online presence of burn centers in Germany, Austria, and Switzerland based on 37 quality criteria. This study aimed to re-evaluate these websites to assess their development over the past 6.5 years.

MSeqDR mvTool: A mitochondrial DNA Web and API resource for comprehensive variant annotation,universal nomenclature collation,and reference genome conversion

Accurate mitochondrial DNA (mtDNA) variant annotation is essential for the clinical diagnosis of diverse human diseases. Substantial challenges to this process include the inconsistency in mtDNA nomenclatures, the existence of multiple reference genomes, and a lack of reference population frequency data. Clinicians need a simple bioinformatics tool that is user‐friendly, and bioinformaticians need a powerful informatics resource for programmatic usage. Here, we report the development and functionality of the MSeqDR mtDNA Variant Tool set (mvTool), a one‐stop mtDNA variant annotation and analysis Web service. mvTool is built upon the MSeqDR infrastructure ( https://mseqdr.org ), with contributions of expert curated data from MITOMAP ( https://www.mitomap.org ) and HmtDB ( https://www.hmtdb.uniba.it/hmdb ). mvTool supports all mtDNA nomenclatures, converts variants to standard rCRS‐ and HGVS‐based nomenclatures, and annotates novel mtDNA variants. Besides generic annotations from dbNSFP and Variant Effect Predictor (VEP), mvTool provides allele frequencies in more than 47,000 germline mitogenomes, and disease and pathogenicity classifications from MSeqDR, Mitomap, HmtDB and ClinVar (Landrum et al., 2013). mvTools also provides mtDNA somatic variants annotations. “mvTool API” is implemented for programmatic access using inputs in VCF, HGVS, or classical mtDNA variant nomenclatures. The results are reported as hyperlinked html tables, JSON, Excel, and VCF formats. MSeqDR mvTool is freely accessible at https://mseqdr.org/mvtool.php .     

Vaccine hesitancy and Web 2.0: Exploring how attitudes and beliefs about influenza vaccination are exchanged in online threaded user comments

The growth of Web 2.0 has been particularly impactful in shaping information assessment in decision-making with regards to vaccination. The aim of the present study was to explore how attitudes and beliefs about influenza vaccination are exchanged in Web 2.0 through an analysis of user comment threads in response to related news reports on the Canadian Broadcasting Corporation national news website (average of 5.8 million unique visitors per month). User comments (n?=?2042) were extracted using a Google Chrome data mining extension, from 33 articles reporting on the seasonal influenza vaccine between September 2015 and October 2016. User comments were analyzed using thematic discourse analysis to identify themes within the data, and also identify how information is exchanged, including identifying the rhetorical devices and tactics used. Mostly unrelated to article content, user comments were extremely polarized with only those with strong positions at either end of the vaccination spectrum (for or against) engaging actively in online debates. Observed exchanges, and the use of rhetorical devices and tactics employed by users are identified as furthering or reinforcing polarization. In addition to exchanging information, forums served as ‘echo chambers’ where individuals connect with likeminded users and collect additional information to reinforce pre-existing beliefs, rather than encouraging the enrichment of user knowledge. Our data lead us to question existing calls for public health engagement in such online forums, as doing so may actually reduce the intention to vaccinate among individuals against vaccination. Rather, we identify a greater need to observe online platforms to better understand the social mechanisms that may contribute to, or reinforce, attitudes and beliefs related to influenza vaccine refusal. Further research may also explore the effect that such dialogue has on the attitudes and beliefs of passively observing individuals who have yet to decide whether to receive the flu vaccine.     

Automatic Identification of Web-Based Risk Markers for Health Events

Background

The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual’s risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors.

Objective

Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy.

Methods

Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user’s search for a query category, and compare to the incidence during other periods for the same individuals.

Results

Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV).

Conclusions

Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.     

Using the Consolidated Framework for Implementation Research to Identify Barriers and Facilitators for the Implementation of an Internet-Based Patient-Provider Communication Service in Five Settings: A Qualitative Study

Background

Although there is growing evidence of the positive effects of Internet-based patient-provider communication (IPPC) services for both patients and health care providers, their implementation into clinical practice continues to be a challenge.

Objective

The 3 aims of this study were to (1) identify and compare barriers and facilitators influencing the implementation of an IPPC service in 5 hospital units using the Consolidated Framework for Implementation Research (CFIR), (2) assess the ability of the different constructs of CFIR to distinguish between high and low implementation success, and (3) compare our findings with those from other studies that used the CFIR to discriminate between high and low implementation success.

Methods

This study was based on individual interviews with 10 nurses, 6 physicians, and 1 nutritionist who had used the IPPC to answer messages from patients.

Results

Of the 36 CFIR constructs, 28 were addressed in the interviews, of which 12 distinguished between high and low implementation units. Most of the distinguishing constructs were related to the inner setting domain of CFIR, indicating that institutional factors were particularly important for successful implementation. Health care providers’ beliefs in the intervention as useful for themselves and their patients as well as the implementation process itself were also important. A comparison of constructs across ours and 2 other studies that also used the CFIR to discriminate between high and low implementation success showed that 24 CFIR constructs distinguished between high and low implementation units in at least 1 study; 11 constructs distinguished in 2 studies. However, only 2 constructs (patient need and resources and available resources) distinguished consistently between high and low implementation units in all 3 studies.

Conclusions

The CFIR is a helpful framework for illuminating barriers and facilitators influencing IPPC implementation. However, CFIR’s strength of being broad and comprehensive also limits its usefulness as an implementation framework because it does not discriminate between the relative importance of its many constructs for implementation success. This is the first study to identify which CFIR constructs are the most promising to distinguish between high and low implementation success across settings and interventions. Findings from this study can contribute to the refinement of CFIR toward a more succinct and parsimonious framework for planning and evaluation of the implementation of clinical interventions.

ClinicalTrial

Clinicaltrials.gov NCT00971139; http://clinicaltrial.gov/ct2/show/NCT00971139 (Archived by WebCite at http://www.webcitation.org/6cWeqN1uY)     

Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

Background

User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics.

Objective

This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics.

Methods

Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance.

Results

Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis.

Conclusions

Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content.     

Disease Detection or Public Opinion Reflection? Content Analysis of Tweets,Other Social Media,and Online Newspapers During the Measles Outbreak in the Netherlands in 2013

Background

In May 2013, a measles outbreak began in the Netherlands among Orthodox Protestants who often refuse vaccination for religious reasons.

Objective

Our aim was to compare the number of messages expressed on Twitter and other social media during the measles outbreak with the number of online news articles and the number of reported measles cases to answer the question if and when social media reflect public opinion patterns versus disease patterns.

Methods

We analyzed measles-related tweets, other social media messages, and online newspaper articles over a 7-month period (April 15 to November 11, 2013) with regard to topic and sentiment. Thematic analysis was used to structure and analyze the topics.

Results

There was a stronger correlation between the weekly number of social media messages and the weekly number of online news articles (P<.001 for both tweets and other social media messages) than between the weekly number of social media messages and the weekly number of reported measles cases (P=.003 and P=.048 for tweets and other social media messages, respectively), especially after the summer break. All data sources showed 3 large peaks, possibly triggered by announcements about the measles outbreak by the Dutch National Institute for Public Health and the Environment and statements made by well-known politicians. Most messages informed the public about the measles outbreak (ie, about the number of measles cases) (93/165, 56.4%) followed by messages about preventive measures taken to control the measles spread (47/132, 35.6%). The leading opinion expressed was frustration regarding people who do not vaccinate because of religious reasons (42/88, 48%).

Conclusions

The monitoring of online (social) media might be useful for improving communication policies aiming to preserve vaccination acceptability among the general public. Data extracted from online (social) media provide insight into the opinions that are at a certain moment salient among the public, which enables public health institutes to respond immediately and appropriately to those public concerns. More research is required to develop an automatic coding system that captures content and user’s characteristics that are most relevant to the diseases within the National Immunization Program and related public health events and can inform official responses.     

基于分布式技术的中西医信息资源整合平台设计

针对目前中医和西医信息资源集成整合的难题,本文在研究分布式相关技术的基础上,采用基于SOA架构的Web 服务技术,将相同疾病的中西医治疗方案相关联,提出了基于分布式技术的中西医信息资源整合模型,并进行了深入分析设计,解决了专业中医或西医医生较难利用中西医结合方案的难题,实现了中西医信息资源的集成性、重用性和扩展性,为现代医学发展提供了重要的参考价值。