Policy Analysis for Prevention and Control of Influenza in Aged Care

ObjectiveThis study aimed to analyze national influenza infection control policy documents within aged care settings by identifying the consistencies, inconsistencies, and gaps with the current evidence and by evaluating methodological quality. Aged care providers can use these findings to identify their policy documents' strengths and weaknesses.DesignA quality and content analysis of national level policy documents.Setting and ParticipantsAged care settings rely on national agencies' policy recommendations to control and prevent outbreaks. There is limited research on the effectiveness of control measures to prevent and treat influenza within aged care settings. Because of the complexities around aged care governance, the primary responsibility in developing a comprehensive facility-level, infection-prevention policy, falls to the providers.MethodsThe analysis was conducted using the (1) International Appraisal of Guidelines, Research and Evaluation assessment tool, containing 23 items across 6 domains; and the (2) Influenza Related Control Measures in Aged Care settings checklist, developed by the authors, with 82 recommendations covering: medical interventions, nonmedical interventions, and physical layout.ResultsThere were 19 documents from 9 different high-income countries, with a moderately high methodological quality in general. The quality assessment's average score was 40.2% (95% CI 31.9%–44.7%). “Stakeholder involvement” ranked third, and “Editorial independence” and “Rigor of development” had the lowest average scores across all domains. The content analysis' average score was 37.2% (95% CI 10.5%–21.5%). The highest scoring document (59.1%) included term definitions, cited evidence for recommendations, and clear measurable instructions. “Physical Layout” had the least coverage and averaged 21.9% (95% CI 4.2%–37.5%), which shows a substantial gap in built environment recommendations.Conclusions and ImplicationsExisting policy documents vary in their comprehensiveness. The higher scoring documents provide an ideal model for providers. The checklist tools can be used to assess and enhance documents. Further research on document end-user evaluation would be useful, as there is room for improvement in methodological quality and coverage of recommendation coverage, especially related to physical layout.     

Atrial fibrillation after cardiac surgery: A systematic review and meta-analysis

ObjectiveNew-onset postoperative atrial fibrillation (POAF) after cardiac surgery is common, with rates up to 60%. POAF has been associated with early and late stroke, but its association with other cardiovascular outcomes is less known. The objective was to perform a meta-analysis of the studies reporting the association of POAF with perioperative and long-term outcomes in patients with cardiac surgery.MethodsWe performed a systematic review and a meta-analysis of studies that presented outcomes for cardiac surgery on the basis of the presence or absence of POAF. MEDLINE, EMBASE, and the Cochrane Library were assessed; 57 studies (246,340 patients) were selected. Perioperative mortality was the primary outcome. Inverse variance method and random model were performed. Leave-one-out analysis, subgroup analyses, and metaregression were conducted.ResultsPOAF was associated with perioperative mortality (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.58-2.33), perioperative stroke (OR, 2.17; 95% CI, 1.90-2.49), perioperative myocardial infarction (OR, 1.28; 95% CI, 1.06-1.54), perioperative acute renal failure (OR, 2.74; 95% CI, 2.42-3.11), hospital (standardized mean difference, 0.80; 95% CI, 0.53-1.07) and intensive care unit stay (standardized mean difference, 0.55; 95% CI, 0.24-0.86), long-term mortality (incidence rate ratio [IRR], 1.54; 95% CI, 1.40-1.69), long-term stroke (IRR, 1.33; 95% CI, 1.21-1.46), and longstanding persistent atrial fibrillation (IRR, 4.73; 95% CI, 3.36-6.66).ConclusionsThe results suggest that POAF after cardiac surgery is associated with an increased occurrence of most short- and long-term cardiovascular adverse events. However, the causality of this association remains to be established.     

我国家庭医生签约服务政策执行的制约因素与优化路径：基于史密斯政策执行过程模型

深化家庭医生签约服务是深化医药卫生体制改革、强化基层医疗卫生服务、实现"健康中国"战略目标的重要选择，也是当前更好维护人民群众健康的重要途径。为有效推进签约服务工作，国家陆续推出各项政策，全国各地也在积极进行实践探索，成效明显。但是，签约服务仍面临诸多问题，其中"执行难"是签约服务深度推进的一大困境。通过史密斯政策执行过程模型，结合签约服务政策执行过程，发现签约服务仍存在法治性不足、政策执行人员水平不高、激励不足、政策环境影响等诸多制约因素。因此，需要从法律和制度方面进行顶层设计、提升执行人员素质和职业认同、建立医患互信、优化政策执行环境等角度进行政策创新，探索家庭医生签约服务可持续发展的路径。     

Análisis de supervivencia en investigación cardiovascular (I): lo esencial

This review provides a practical guide to the essentials of survival analysis and their reporting in cardiovascular studies, although most of its key content can be extrapolated to other medical fields. This is the first in a series of 2 educational articles laying the groundwork to address the most relevant statistical issues in survival analyses, which will smoothly drive the reader from the most basic analyses to the most complex situations. The focus will be on the type and shape of survival data, and the most common statistical methods, such as nonparametric, parametric and semiparametric models. Their adequacy, interpretation, advantages and disadvantages are illustrated by examples from the field of cardiovascular research. This article ends with a set of recommendations to guide the strategy of survival analyses for a randomized clinical trial and observational studies. Other topics, such as competing risks, multistate models and recurrent-event methods will be addressed in the second article.     

2011—2020年呼和浩特市流行性腮腺炎流行特征分析

目的 分析呼和浩特市2011—2020年流行性腮腺炎(流腮)流行病学特征,为制定有针对性防控措施提供参考依据。方法 对呼和浩特市2011—2020年流行性腮腺炎的发病数据进行监测分析,描述其分布及趋势。 呼和浩特市2011—2020年共报告流腮病例6 176例,年均发病率20.60/10万,其中2012年发病率最高为40.17/10万,2020年发病率最低为7.08/10万,2012年和2020年发病率差异有统计学意义(χ2=717.641,P<0.001),10年间流腮发病呈波动下降趋势。流腮发病具有明显季节性,11月至次年1月和4—7月出现2个发病高峰。年龄分布以儿童和青少年为主,占总病例数60.83%。流腮病例最多人群是学生,占总病例数的61.08%。 流腮暴发疫情主要发生在中小学校,应加强儿童入托、入学预防接种证查验和流腮疫苗查漏补种工作,必要时开展6～15岁学生为目标人群的应急接种。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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