Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

Introduction

The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics.

Methods

We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students’ beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network.

Results

Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics.

Conclusions

This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student’s perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.     

Healthcare Worker Contact Networks and the Prevention of Hospital-Acquired Infections

We present a comprehensive approach to using electronic medical records (EMR) for constructing contact networks of healthcare workers in a hospital. This approach is applied at the University of Iowa Hospitals and Clinics (UIHC) – a 3.2 million square foot facility with 700 beds and about 8,000 healthcare workers – by obtaining 19.8 million EMR data points, spread over more than 21 months. We use these data to construct 9,000 different healthcare worker contact networks, which serve as proxies for patterns of actual healthcare worker contacts. Unlike earlier approaches, our methods are based on large-scale data and do not make any a priori assumptions about edges (contacts) between healthcare workers, degree distributions of healthcare workers, their assignment to wards, etc. Preliminary validation using data gathered from a 10-day long deployment of a wireless sensor network in the Medical Intensive Care Unit suggests that EMR logins can serve as realistic proxies for hospital-wide healthcare worker movement and contact patterns. Despite spatial and job-related constraints on healthcare worker movement and interactions, analysis reveals a strong structural similarity between the healthcare worker contact networks we generate and social networks that arise in other (e.g., online) settings. Furthermore, our analysis shows that disease can spread much more rapidly within the constructed contact networks as compared to random networks of similar size and density. Using the generated contact networks, we evaluate several alternate vaccination policies and conclude that a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy.     

积极的防护意识与健全的防护体系在传染病医院内的影响与效应

目的:通过提高防护意识和进一步完善防护体系实现对传染病医院的全面管理.方法:随机将我院2009年3月至2011年11月的两个病区患者及医护人员作为研究对象,向所有患者发放调查表对政策调整前后的满意程度进行调查,通过针对医护人员防护意识的强化教育和医院设施及防护体系的调整与完善以评估对患者住院期间的临床效应与影响.结果:全院上下的积极防护意识普遍增强并完善健全了防护体系,从而显著降低了感染率,患者满意度反馈良好.结论:通过提高医护人员防护意识和健全医院防护体系有助于进一步减少院内感染,提高医院医疗质量和服务水平.     

医务人员手卫生监测结果回顾性分析及对策

目的了解医务人员手部卫生状况,制定改进措施,预防医院感染。方法回顾性分析东南大学附属中大医院2007年1月至2010年12月医务人员手卫生监测资料1586份。结果医护手监测合格率为89．28％,医生手监测合格率为86．94％,护士手监测合格率为93．09％;重点病区医护手监测台格率为91．98％,普通病区医护手监测合格率为87．06％;该院医护手监测合格率为90．64％,进修、实习医护手监测合格率为88．17％,手部检出细菌184株,葡萄球菌属占49．46％,阳性杆菌占14．67％。结论医务人员对手的清洁与消毒仍缺乏足够的认识,应加强医务人员手卫生培训与监督,提高医务人员手卫生合格率。     

医院核心价值观的构建与思考

近年来,群众反映强烈的“看病难、看病贵”、收取红包、开大处方及乱收费等问题日益突显,引起医患关系日趋紧张。虽然存在着种种原因,但医院核心价值观缺失、医学科学人文精神弱化便是其中之一,如何构建起医院核心价值观成了当下一个重要话题。从医院、医院管理层和广大职工三个层面去凝炼医院的核心价值观,阐释构建医院核心价值观的重要意义和途径。     

An Emergency Psychiatric Service in a General Hospital

The same extensive range of general hospital facilities should be allocated to emergency psychiatric illness as are available for other medical conditions. During the study herein reported, for every three medical consultations in the emergency ward of a large general hospital, two psychiatric consultations were requested. Over a two-year period when 24-hour coverage by psychiatric consultants was instituted, such assessments increased from 148 to 340 (during the first four months of each year); the increase in police referrals was outstanding, rising from 16 to 105. The general wards of the hospital assumed greater responsibility for further medical treatment, while committal to the mental hospital declined. Many more psychiatric patients could have been treated in the general hospital if facilities had been available. The development of an emergency psychiatric service is not an easy process and co-ordination with other psychiatric resources is required. Residents in training face situations in the emergency ward which are not encountered in any other aspect of their clinical experience.     

A survey of Pneumocystis carinii infection in research mouse colonies in Japan.

To determine the frequency of Pneumocystis carinii infection in mouse colonies maintained for biomedical research in medical colleges or medical faculties in universities in Japan, 409 nu/nu mice were sent to 43 animal facilities from a P. carinii-free colony. The animals were housed for 6 months in groups of 3 to 10 animals per room, and examined for the presence of parasites and infection. Colonies in 10 (24.4%) of 41 facilities were positive for the infection. Of 383 animals in 69 rooms, the organism was detected in 66 (17.2%) animals in 13 (18.8%) rooms. The difference in the proportion of rooms where mice were positive for P. carinii is clearly seen among these three groups; SPF mouse rooms (4 of 38 rooms, 10.5%), SPF mouse rooms with breeding units (5 of 25 rooms, 20.0%) and conventional mouse rooms (4 of 6 rooms, 66.7%). The survey indicates that strict housing arrangements and husbandry techniques are necessary to keep SPF mice free from P. carinii infection.     

Mathematical modeling of severe acute respiratory syndrome nosocomial transmission in Japan: the dynamics of incident cases and prevalent cases

An outbreak of Severe Acute Respiratory Syndrome (SARS) occurred in Hong Kong in late February 2003, resulting in 8,096 cumulative cases with 774 deaths. The outbreak was amplified by nosocomial transmission in many hospitals. Using mathematical modeling, we simulated the number of new incident and prevalent cases of SARS after one infected person was admitted to a hospital (index case). The simulation was tested stochastically using the SEIR model based on previously reported Gamma distributions. We estimated the duration time until 10 beds in negative pressure rooms in Chiyoda-ku, one of the 23 wards in Tokyo, were fully occupied with SARS-infected patients. We determined the impact of an increasing number of days on the number of prevalent cases until the index case was isolated. The prevalent cases increase exponentially along with the increase of the non-isolation period of the index case, and all the beds were fully occupied if the index case was not isolated until more than 6 days. However even 2 days non-isolation period of the index case could fill up all the beds when 16% of secondary infections are transmitted outside the hospital. There is a possibility that an epidemic will occur with the isolation of the index case even at early days if the infection is transmitted outside the hospital. The simulation results revealed that it was important to recognize and isolate SARS patients as early as possible and also to prevent the transmission spreading outside the hospital to control an epidemic.     

Patients in acute surgical wards: a survey in Glasgow.

A survey was made of all patients in general surgical, urological, and orthopaedic and accident wards in Glasgow on one day in June 1975. Its purpose was to define features of acute surgical practice of relevance to the future planning of resources, particularly bed numbers. Over 40% of the patients in both surgical and orthopaedic wards were over 65 years. Most patients had serious conditions and could not have been treated other than by admission to an acute surgical ward. But a substantial minority no longer needed such facilities and could have been transferred to second-line beds, although many still required skilled nursing care. Delay in the discharge of elderly patients from acute surgical wards as a consequence of non-surgical (often medical or social) problems results in a proportion of acute surgical beds fulfilling a second-line function. Unless arrangements for the earlier discharge of these patients are made any reduction in acute surgical beds is likely to restrict elective surgery, especially in orthopaedics.     

Epidemics in Interconnected Small-World Networks

Networks can be used to describe the interconnections among individuals, which play an important role in the spread of disease. Although the small-world effect has been found to have a significant impact on epidemics in single networks, the small-world effect on epidemics in interconnected networks has rarely been considered. Here, we study the susceptible-infected-susceptible (SIS) model of epidemic spreading in a system comprising two interconnected small-world networks. We find that the epidemic threshold in such networks decreases when the rewiring probability of the component small-world networks increases. When the infection rate is low, the rewiring probability affects the global steady-state infection density, whereas when the infection rate is high, the infection density is insensitive to the rewiring probability. Moreover, epidemics in interconnected small-world networks are found to spread at different velocities that depend on the rewiring probability.     

Modelling of the epidemic process of suppurative septic infections using a Pseudomonas aeruginosa bacteriophage

The method for modeling the epidemic process of pyoseptic infections with the use of P. aeruginosa bacteriophage is proposed. The application of this method in urological and traumatological wards has made it possible to confirm the role of patients as the sources of infection and the part played by instruments and the hands of the medical personnel in its transfer.     

Network structure, and vaccination strategy and effort interact to affect the dynamics of influenza epidemics

There is growing interest in understanding and controlling the spread of diseases through realistically structured host populations. We investigate how network structures, ranging from circulant, through small-world networks, to random networks, and vaccination strategy and effort interact to influence the proportion of the population infected, the size and timing of the epidemic peak, and the duration of the epidemic. We found these three factors, and their higher-order interactions, significantly influenced epidemic development and extent. Increasing vaccination effort (from 0% to 90%) decreased the number of hosts infected while increasing network randomness worked to increase disease spread. On average, vaccinating hosts based on degree (hubs) resulted in the smallest epidemics while vaccinating hosts with the highest clustering coefficient resulted in the largest epidemics. In a targeted test of five vaccination strategies on a small-world network (probability of rewiring edges=5%) with 10% vaccination effort we found that vaccinating hosts preferentially with high-clustering coefficients (similar to real-world strategies) resulted in twice the number of hosts infected as random vaccinations and nearly a 30-fold higher number of cases than our strategy targeting hubs (highest degree hosts). Our model suggests how vaccinations might be implemented to minimize the extent of an epidemic (e.g., duration and total number infected) as well as the timing and number of hosts infected at a given time over a wide range of structured host networks.     

Survey of "do not resuscitate" orders in a district general hospital.

OBJECTIVE--To evaluate the local use of written "Do not resuscitate" orders to designate inpatients unsuitable for cardiopulmonary resuscitation in the event of cardiac arrest. DESIGN--Point prevalence questionnaire survey of inpatients'' medical and nursing records. SETTING--10 acute medical and six acute surgical wards of a district general hospital. PARTICIPANTS--Questionnaires were filled in anonymously by nurses and doctors working on the wards surveyed. MAIN OUTCOME MEASURES--Responses to questionnaire items concerning details about each patient, written orders not to resuscitate in the medical case notes and nursing records, whether prognosis had been discussed with patients'' relatives, whether a "crash call" was perceived as appropriate for each patient, and whether the "crash team" would be called in the event of arrest. RESULTS--Information was obtained on 297 (93.7%) of 317 eligible patients. Prognosis had been discussed with the relatives of 32 of 88 patients perceived by doctors as unsuitable for resuscitation. Of these 88 patients, 24 had orders not to resuscitate in their medical notes, and only eight of these had similar orders in their nursing notes. CONCLUSIONS--In the absence of guidelines on decisions about resuscitation, orders not to resuscitate are rarely included in the notes of patients for whom cardiopulmonary resuscitation is thought to be inappropriate. Elective decisions not to resuscitate are not effectively communicated to nurses. There should be more discussion of patients'' suitability for resuscitation between doctors, nurses, patients, and patients'' relatives. Suitability for resuscitation should be reviewed on every consultant ward round.     

Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.     

What's in a crowd? Analysis of face-to-face behavioral networks

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks.     

Policy Resistance Undermines Superspreader Vaccination Strategies for Influenza

Theoretical models of infection spread on networks predict that targeting vaccination at individuals with a very large number of contacts (superspreaders) can reduce infection incidence by a significant margin. These models generally assume that superspreaders will always agree to be vaccinated. Hence, they cannot capture unintended consequences such as policy resistance, where the behavioral response induced by a new vaccine policy tends to reduce the expected benefits of the policy. Here, we couple a model of influenza transmission on an empirically-based contact network with a psychologically structured model of influenza vaccinating behavior, where individual vaccinating decisions depend on social learning and past experiences of perceived infections, vaccine complications and vaccine failures. We find that policy resistance almost completely undermines the effectiveness of superspreader strategies: the most commonly explored approaches that target a randomly chosen neighbor of an individual, or that preferentially choose neighbors with many contacts, provide at best a relative improvement over their non-targeted counterpart as compared to when behavioral feedbacks are ignored. Increased vaccine coverage in super spreaders is offset by decreased coverage in non-superspreaders, and superspreaders also have a higher rate of perceived vaccine failures on account of being infected more often. Including incentives for vaccination provides modest improvements in outcomes. We conclude that the design of influenza vaccine strategies involving widespread incentive use and/or targeting of superspreaders should account for policy resistance, and mitigate it whenever possible.     

风险预判与主动干预在综合ICU患者多重耐药菌医院感染防控中的关键作用研究

目的:阐述风险预判与主动干预的综合防控措施对综合重症监护病房(GICU)患者多重耐药菌(MDRO)医院感染的防控效果。方法:对2018年1月~2019年12月入住GICU病房(分为A、B两个病区)>48 h的737例患者进行回顾性调查,其中A病区监测患者286例,MDRO防控参照院感科常规制度要求;B病区监测患者451例,MDRO防控采用入GICU时预判患者感染风险,再根据感染风险及患者自身状况对患者采取鼻腔去定植或肠道去定植的综合防控策略。用卡方检验比较两病区患者的感染结果与MDRO感染菌种分布情况,以验证不同防控策略的效果。结果:本研究共监测GICU住院患者737例,研究期间共发生MDRO医院感染85例。其中A病区监测患者286例,MDRO医院感染66例,感染率为23.1%;B病区监测患者451例,MDRO医院感染19例,感染率为4.2%,低于A病区(P<0.001)。单菌种感染结果显示,两病区感染菌种分布存在差异,CR-AB、CR-PA和MASR的感染率都为B病区低于A病区,两病区患者的共患病类型无差异。说明B病区MDRO防控效果优于A病区。结论:感染风险预判与主动干预的综合防控策略,有利于降低GICU患者MDRO医院感染发病率。     

Absenteeism among hospital staff during an influenza epidemic: implications for immunoprophylaxis.

The 1980-81 epidemic of influenza A/Bangkok 79 was responsible for increased absenteeism (1.7 times the rate for the corresponding period of the subsequent nonepidemic year) among selected hospital staff in Winnipeg''s Health Sciences Centre. Retrospective study of employment records for 25 of the centre''s largest departments showed excess sick-leave costs of about $24 500 during the 2-week period of peak absenteeism that included the epidemic. Although the centre was sampling prospectively for the virus the first positive results became available too late for chemoprophylactic measures to have been effective. The greater increase in absenteeism among nursing staff caring for patients with chronic respiratory disease and nurses working on general medical or pediatric acute infection/isolation wards suggested that these groups be targeted for influenza vaccination in hospitals.     

The smallpox vaccine: a multidimensional model of choice

Following September 11, 2001, the U.S. government increased its efforts to prepare for future attacks, including those using dangerous biological agents such as smallpox. The smallpox vaccination program called for vaccinating military personnel and smallpox response teams, including healthcare workers and other first responders. The program of vaccinating healthcare workers was largely unsuccessful; few individuals volunteered to be vaccinated, highlighting the importance of understanding the factors that influence choice regarding this complex medical decision. This study examined stated choice and how it was associated with risk perceptions, knowledge, psychological distress, and general vaccine beliefs using a five-dimensional choice model. The model used multivariable modeling strategies in a sample of 256 undergraduate, graduate, and medical students. Sixty-three percent of the sample stated that they would elect to receive the smallpox vaccination. Multiple factors were related to stated choice in multivariable models, including perceived risk/worry, general vaccine beliefs, decisional conflict, and gender. However, the models were more successful at predicting acceptance of the vaccination than vaccine refusal. Although support was obtained for a multidimensional model of choice, several questions were raised by our results, including (a) whether refusal of smallpox vaccination can be more effectively characterized, possibly with additional questions; (b) whether the model translates to actual vaccination behavior; and (c) whether the model describes choice in more at-risk samples (e.g., first responders, healthcare workers). A multidimensional modeling approach should facilitate these and other studies of choice.