Social media use and influenza vaccine uptake among White and African American adults

Influenza vaccination rates in the U.S. remain low at 41% among adults over 18 years according to the Centers for Disease Control and Prevention’s 2016 data. Reasons for the low rate vary and include factors such as risk perception, vaccine hesitancy, and access to health care. This cross-sectional study sought to examine the relationship between social media use and influenza vaccine uptake among a sample of White and African Americans over 18 years of age. Using bivariate, and unadjusted and adjusted logistic regression tests, this study examined the relationship among social media use, social media as a source of health information, and influenza vaccination status in 2015. Our results indicate that users of Twitter (OR4.41, 95%CI: 1.43–13.60) and Facebook (OR 1.66, 95%CI: 1.01–2.72) as sources of health information were more likely to be vaccinated in comparison to users who do not use Twitter or Facebook as a source of health information. These findings have implications for the potential of using social media platforms to disseminate influenza vaccine information and encourage users to get vaccinated annually.     

Use of information communication technology and stress,burnout, and mental health in older,middle-aged,and younger workers – results from a systematic review

The aim of this systematic review is to summarise quantitative studies in occupational settings observing the association between Information communication technology (ICT) and stress, and burnout, considering age as an effect modifier. A systematic review using PRISMA guidelines was conducted through the following bibliographic databases: PubMed, Web of Science, Psycinfo, and the Cochrane Library. Inclusion criteria were occupational settings and content relevant to our research question. Risk of bias was assessed using the Newcastle–Ottawa scale. Two interventional, 4 cohorts, and 29 cross-sectional studies were found. ICT use in occupational settings was associated with stress seen in cross-sectional studies, but not in interventional studies. There was a concordant association with ICT and burnout in different study designs. Overall, there were no linear trends between age and technostress. We suggest that the observed associations were mostly present in the middle-aged working population and that these associations need to be supported in further studies.     

医院感染监测系统的开发与应用

通过与软件公司联合开发满足医院要求的感染管理软件,实现感染管理的信息化.其功能包括疑似病例智能筛查、多重耐药菌监测、抗菌药物监测、专职人员与临床医生交互平台等.结果表明运用该软件系统能及时提供医院感染动态信息,提高工作效率,改善医院感染管理水平.     

医院图书馆信息服务面临的问题与对策

如今已进入信息时代,在信息技术飞速发展的推动下,各行各业的发展模式推陈出新,图书馆同样如此,传统的图书馆服务模式在信息服务的改变下,服务水平得到了有效提升。文章就当前我国医院图书馆信息服务中面临的问题展开研究分析,探讨针对的解决对策。     

The comprehensive diagnostic study is suggested as a design to model the diagnostic process

ObjectivesThe classical diagnostic cross-sectional study has a focus on one disease only. Generalist clinicians, however, are confronted with a wide range of diagnoses. We propose the “comprehensive diagnostic study design” to evaluate diagnostic tests regarding more than one disease outcome.Study Design and SettingWe present the secondary analysis of a data set obtained from patients presenting with chest pain in primary care. Participating clinicians recorded 42 items of the history and physical examination. Diagnostic outcomes were reviewed by an independent panel after 6-month follow-up (n = 710 complete cases). We used Shannon entropy as a measure of uncertainty before and after testing. Four different analytical strategies modeling specific clinical ways of reasoning were evaluated.ResultsAlthough the “global entropy” strategy reduced entropy most, it is unlikely to be of clinical use because of its complexity. “Inductive” and “fixed-set” strategies turned out to be efficient requiring a small amount of data only. The “deductive” procedure resulted in the smallest reduction of entropy.ConclusionWe suggest that the comprehensive diagnostic study design is a feasible and valid option to improve our understanding of the diagnostic process. It is also promising as a justification for clinical recommendations.     

Extracting general practice data for timely vaccine coverage estimates: The England experience

In England, primary care providers use standardised coding systems to record health events such as vaccination as well as patient characteristics. This information can be automatically extracted to estimate coverage for vaccine programmes delivered through primary care, in the general population as well as in specific geographical, ethnic, age or clinical groups. This system provides timely vaccine coverage estimates as well as the flexibility to extract tailored data in order to directly inform a continuously evolving national vaccine programme. It is however limited by the quality and completeness of clinical coding in primary care. A centralised, individual-level register would however improve data quality, completeness and reliability and remains the gold standard.     

Utility-preserving privacy protection of textual healthcare documents

The adoption of ITs by medical organisations makes possible the compilation of large amounts of healthcare data, which are quite often needed to be released to third parties for research or business purposes. Many of this data are of sensitive nature, because they may include patient-related documents such as electronic healthcare records. In order to protect the privacy of individuals, several legislations on healthcare data management, which state the kind of information that should be protected, have been defined. Traditionally, to meet with current legislations, a manual redaction process is applied to patient-related documents in order to remove or black-out sensitive terms. This process is costly and time-consuming and has the undesired side effect of severely reducing the utility of the released content. Automatic methods available in the literature usually propose ad-hoc solutions that are limited to protect specific types of structured information (e.g. e-mail addresses, social security numbers, etc.); as a result, they are hardly applicable to the sensitive entities stated in current regulations that do not present those structural regularities (e.g. diseases, symptoms, treatments, etc.). To tackle these limitations, in this paper we propose an automatic sanitisation method for textual medical documents (e.g. electronic healthcare records) that is able to protect, regardless of their structure, sensitive entities (e.g. diseases) and also those semantically related terms (e.g. symptoms) that may disclose the former ones. Contrary to redaction schemes based on term removal, our approach improves the utility of the protected output by replacing sensitive terms with appropriate generalisations retrieved from several medical and general-purpose knowledge bases. Experiments conducted on highly sensitive documents and in coherency with current regulations on healthcare data privacy show promising results in terms of the practical privacy and utility of the protected output.