The objective of this paper is 1) to argue for the video observation technique to inform and improve health-information-technology development and 2) to share insights and lessons learned on benefits and challenges when using the video observation technique within healthcare settings.Experience shows that the precondition for the development of successful health information technologies is a thorough insight into clinical work practice. In contemporary clinical work practice, clinical work and health information technology are integrated, and part of the practice is tacit. When work practice becomes routine, it slips to the background of the conscious awareness and becomes difficult to recognize without the context to support recall. This means that it is difficult to capture with traditional ethnographic research methods or in usability laboratories or clinical set ups. Observation by the use of the video technique within different healthcare settings.A multiple case study including nine case studies conducted by DaCHI researchers 2004–2011 using audio-visual, non-participant video observation for data collection In HIT development, video observation is beneficial for 1) informing and improving system design 2) studying changes in work practice 3) identifying new potentials and 4) documenting current work practices.The video observation technique used within healthcare settings is superior to other ethnographic research methods when it comes to disclosing the complexity in clinical work practice. The insights gained are far more realistic compared to traditional ethnographic studies or usability studies and studies in clinical set ups. Besides, the data generated through video recordings provide a solid basis for dialog between the health care professionals involved. The most important lessons learned are that a well considered methodology and clear formulated objectives are imperative, in order to stay focused during the data rich analysis phase. Additionally, the video observation technique is primarily recommended for studies of specific clinical work practices within delimited clinical settings. Overall, the video observation technique has proven to be capable of improving our understanding of the interwoven relation between clinical work practice and HIT and to inform us about user requirements and needs for HIT, which is a precondition for the development of more successful HIT systems in the future. However, the history of HIT shows that HIT projects are not only beneficial, but often fail to meet their goals -3 despitfactors”  p.291. Sfactors” ,6. Studifactors” -9. Becaurovided”  p. 435. a) at the Department of Development and Planning at Aalborg University, Denmark shows that in contemporary clinical work practice, clinical work and health information technology are closely integrated, and that they should, when studied, be viewed as mutually dependent and not as discrete entities [Research within DaCHI   and the The views, knowledge and experiences presented in this paper are based on a hermeneutical point of view on the clinical sociomaterial work practice. In the specific context of the healthcare sector, this means that we try to understand and to interpret “what the clinicians are doing”, when they perform their daily clinical work. Thus, our research aims to provide a basis for understanding the processes that cause an intervention (HIT development and implementation) to make a difference in a specific context  . One of not mean that the video observation method cannot provide benefits within other study paradigms. One of the cases included in this paper is an example of this, as the objective is both of an explanatory (positivistic) and an understanding / interpreting  nature [However, the fact that the views presented in this paper are based on a hermeneutical study paradigm does ) nature .Below a short overview on the study methodology is first presented. Then, the different aspects are presented in more details:Theoretical approach: Socio-TechnicalDesign: Multiple caseStudy period: 2004–2009Study object: The sociomaterial interactions between users and technology in clinical work practiceData collection method: EthnographicData collection technique: ObservationData collection instrument: VideoData analysis and trustworthiness: See belowThis study is based on a socio-technical approach to studies of the sociomaterial interactions between humans and technology in HIT development. According to this, the implementation of HIT will lead to a mutual and sustained interaction between and transformation of the involved users/organization by the technology, and of the technology by the involved users/organizations. Embedded into the approach is a user-oriented perspective, ,51.within different healthcare settings – ie. at different hospital wards. The nine cases are listed below.The study is a multiple case study, as the experiences presented are based on the results of nine case studies conducted by DaCHI researchers 2004–2011 using audio-visual video observation techniques for data collection 1. Recycling of Administrative Patient Data to measure Health Professional Quality, 2011 2. Evaluation of IT support for the Common Acute Receiving Unit at HorsensRegional Hospital, 2010 3. Evaluation of the ‘Clinical Process' Electronic Health Record (EHR) in theDanish Northern Region, 2010 4. Usability of CPOE (Computer Physician Order Entry System) 2010 5. Studies of clinical work practice illustrated through three cases, 2007 6. Medical Secretaries work practice before the implementation of the EHR, 2005 7. User-influence on the organization of work in a hospital ward before and after the implementation of the EHR, 2004 8. Management of surgical programs, 2004 9. Evaluation of the GEPKA project, 2004 A common characteristic on these case studies is that the video-data were all audio-visual and non-participant.In Table The study object is the sociomaterial interactions between the clinicians and the HIT in clinical work practice.The data collecting method, technique and instrument focused on in this study is ethnographic observation by video. All video observations have been audio-visual. This means that data on the sociomaterial interactions between the clinicians and the technology in clinical work practice have been collected in both picture and sound. Besides, all video observations have been non-participant, meaning that the researcher has had no/minimum interactions with the clinicians and the clinical work practice during data collection.method. Video observation is one out of more observational data collection techniques within ethnographic data collection methods, ranging from non-participant video observation to participant video observation [It is important to stress that we do not consider the video observation technique itself a research ervation -54 , hence as a preparation for the video observations.First, the video sequences relevant according to the study objective have been identified. Next, these sequences have been transcribed and mapped. The interpretation process has been done with a focus on the objective. Trustworthiness has been sought by validating the interpretations by the clinicians participating in the recordings and by thoroughly describing all activities throughout the process (transparency).According to the Danish “Law on the Scientific Committee System and the treatment of biomedical research, chapter 3, paragraph 8, section 3”, a formal ethical approval from “The Danish National Committee on Research Ethics” is only required, if the study includes human biological materials. However, according to the law “Danish Act on Processing of Personal Data” chapter 4, paragraph 6, section1”, informed consent is required from all patients and staff-members, who are video-recorded. Whether this should be in oral or in writing is not specified in the law as stated above. Out of informal ethical considerations, we (researchers within DaCHI) try to avoid to video record patients – and especially patients faces. If a patient’s face by accident is video recorded, we delete the sequence.oral for video-recordings only to be studied by the researchers. For video-sequences to be shown to others at, e.g. scientific meetings, we obtain informed consent in writing from all staff participating in the video.For staff-members, we (researchers within DaCHI) have decided on obtaining informed consent in total experiences from 9 case studies. The individual results are not presented, as the objective of this paper is to argue for the relevance of the video observation technique to provide insight into sociomateriality and by doing so inform and improve health information technology development. Further, the aim is to share our experience on the most important lessons learned on benefits and challenges when using the video observation technique within healthcare settings – and not to present the results of the individual cases, which have quite different objectives .The results presented in this paper are based on the During our studies, we have developed a generic guideline on how to conduct a video observation study:1. contact to the management at hospital level (explain objective and methods)2. contact to the management at ward level (explain objective and methods)3. preliminary visits to wards 4. video observation5. data-analysis and interpretation6. data validation with clinicians and other relevant actors7. data presentationBesides, we have gained thorough experiences on where, when and how to use video observation to inform and to improve HIT development. Based on our experiences, video observation provides benefits in studies with the following objectives:inform and improve the design of new health information technologies through studies of specific clinical work practices in delimited clinical settings1. to study changes of specific clinical work practices before and after the implementation of new health information technologies in delimited clinical settings2. to identify potentials for new ways to organize clinical work practice - including potential labor savings - when implementing new health information technologies through studies of specific clinical work practices in delimited clinical settings3. to document current clinical work practice for future research purposes (e.g. before and after new HIS is implemented) through studies of specific present clinical work practices in delimited clinical settings4. to within healthcare settings.A number of benefits are common to the study objectives listed above - as well as a number of lessons learned on how to manoeuver as a researcher collecting data by use of video observation During our studies, we have found that video observation is most beneficial for studies of specific work practice within delimited clinical settings. Trying to study a wide range of clinical work practice at a number of different clinical settings at once is extremely time consuming and should only be done if based on thorough considerations on why and how to conduct the study.context dependent clinical sociomaterial work practice, often involving staff with different professions in contrast to in usability laboratories or in clinical set-ups. In the latter unforeseen disruptions and communication challenges can be hard to imitate no matter how realistic the clinical setting has been set up [Used within the healthcare sector, video observation permits us to explore n set up -39.In a video observation study on the impact of the Electronic Health Record (EHR) on sharing information’s between nurses at morning meetings, the nurses sought information’s in two different EHR systems, while at the same time making handwritten notes at other schemes and papers. The interactions between the nurses and the different artifacts went on extremely fast. When analyzing the video data, new insights were gained every time, we revisited the field through the sequences of recorded data . Thus, tIn the same study as mentioned above , also thWhen analyzing the data from an explorative study on medical secretaries work practice before the implementation of the EHR together with the involved secretaries, it became obvious that during interviews and personal observations conducted previously, only part of their work practice had been captured, partly because of the complexity, partly because of the routine and tacit nature of their work practice . Thus, cAdditionally, studying the video recordings in collaboration with the clinicians – and other relevant professionals in HIT development – provides an excellent basis for a dialog about understanding clinical work practice.Overall, by providing us a thorough insight into the complexity of sociomaterial clinical work practice, the common benefits on video observation is insight into and a better understanding of the work practice of health care professionals.Below we will present our most important lessons learned during three different phases in HIT- development:the planning phasethe data collection phasethe data analysis and interpretation phaseWe consider the planning phase the most important of all phases, because a well-planned study is crucial to the success of the following phases. Therefore, in this paper more attention is given to the planning phase compared to the other phases.any study – no matter the methods and the techniques used - is the preparation of the study methodology, as this constitutes the “roadmap” from start to end [The first and most important step in t to end . Howevert to end ,53,55-57When conducting research – e.g. evaluation studies - in collaboration with external contracting authorities, the objective is often defined by the client beforehand. According to our experiences, such study objectives are often rather “loosely” formulated. An example is: “to provide an assessment of the clinical benefits of X system”. This objective might seem straightforward at first, but if not elaborated, a number of questions will inevitably arise later on in the study-process:what is meant by “clinical benefits”?from who’s point of view should the benefits be assessed? by which indicators should clinical benefits be measured?Thus, it is necessary to clarify the study objective to avoid any future misunderstandings and - what is also important - to adjust the study objective to meet “real life” constraints when it comes to economic conditions, time, study settings etc. However, a precondition for the researchers to be able to clarify a study objective is a thorough insight into the clinical context in which the study is going to take place. In a hospital setting, this will include:number of wardsnumber of sections in each wardnumber of employees – and professional groups to be involved in the studythe shifts (in a 24/7 setting) relevant to studySome of this information can be achieved through homepages, literature and dialog/interviews with relevant key informants, but – based on our experiences - preliminary visits to the study locations to gain the needed insights through personal observation and interviews are mandatory. Based on the knowledge achieved through site visits etc., the researchers must then reach an agreement on clear and measurable objectives to prevent major problems and misunderstandings later on in the process. First then, the additional elements of the methodology (e.g. design and data collecting methods) should be formulated.When the objective indicates that the video observation technique is beneficial, it is important to reflect and decide on the kind of video observation Figure . HoweverThe next step in the planning phase is to decide on:1. the overall perspective: specific work practices, certain professional groups or…?which specific clinical work practice performed by which professionals at which wards at which time by which researchers?2. the focus: e.g. 3. which camera angels should be used: e.g. should the video be fixed or roving - and does the setting  allow a camera to be placed in the optimal position?4. what is left out by the decisions taken in 1, 2 and 3 - and how does this impact the results?5. how many researchers will do the recordings?6. for how long time  to record?The list above is not exhaustive, but it comprises the issues found most important during our studies. Common to all questions are that the answers are closely linked to the study objectives. Thus, when answering the questions, the study objective must simultaneously be readdressed.An important step during the planning phase – which should be taken as soon as the study objective is formulated - is getting permission to do video recordings within the clinical setting, ie. at the wards. Our experience is that a well prepared study and clear objectives are imperative for achieving the trust and confidentiality of the management necessary to gain permission to do video recordings within a ward.However, having gained permission from the management does not imply that we walk straight into e.g. a hospital ward and start recording. To establish trust and willingness to participate in the study among the clinical staff is an important part of the planning phase. Thus, at preliminary visits at we inform the clinicians on the study objective, what our presence mean to their daily work, what is expected of them, etc. This is an issue, which is also stressed by Heath et al. . During When the planning phase is completed, the next steps in the study process should be far less time consuming – given that the study methodology is well prepared and all the precautions mentioned in this section have been taken.As mentioned in the section on ethical considerations, a very important consideration, when video-recording within a clinical setting, is how to avoid video recording the patient – and especially the patient’s face. When recording within a ward, the optimum solution to this is to place the camera at the headboard of the bed. From this angle, it is possible to capture most activities going on in the room without recording the patient’s face. It can be difficult to avoid recordings of patients, when e.g. following a clinician up and down the corridors with a roving camera while he/she is performing a ward-round. If a patient’s face by accident is video-recorded, we delete that sequence.During recordings, it is important fully to adhere to the methodology formulated in the planning phase. This means that when the observation method decided on is e.g. non-participant observation , the researcher should refrain from interfering with the clinicians and the clinical work practice. If not, the data-validity is compromised, as the data gathered will be influenced by the researcher’s interaction. If the video observation method is participant observation, the researcher may ask the clinicians to reflect on their own practice (fly in the eye). This may provide a clarification and elaboration of socio-material activities, issues and situations while video recording, and thereby provide more in depth and elaborated information of e.g. reasons behind specific clinical work practice. Information, that otherwise would remain tacit knowledge and/or stickyinformation .does affect the way they behave when performing their daily clinical tasks. However, the more and the better the clinicians have been informed on the study objective beforehand, the better we have been able to establish contact and trust - which is a precondition for diminishing this bias. Thus, after a while (often only one-two hours), they seem to be less affected by our presence. This view is supported by Nøhr et al. [It is our experience that when clinical staff is video recorded, they often try to perform certain tasks according to “what the books tell”, instead of how they themselves have appropriated work practice to fit the clinical context and the resources available. Thus, our presence r et al. . We havenot been conducted from a fixed, clear and precise methodology, problems often arise during this phase, because of the researchers loosing focus and missing the “red thread” when navigating through the (high time-consuming) analysis phase. Part of - or in worse case - all the collected data can then prove to be useless, because they prove irrelevant to the study objective. During one of our studies, the objective was revised by external contracting authorities half way through the study. Originally, the objective was to assess the clinical benefits of the fully implemented system from the clinician’s perspective. This was changed into an assessment of the clinical benefits of the implementation of only the first part of the system. This meant that a substantial part of our baseline data became irrelevant [Analyzing video-recorded observation data differs from analyzing data collected through other ethnographic methods by the huge quantity of data – both visual and audial. If the study has relevant .If, on the contrary, the data have been collected from a well-planned methodology, the researchers still have a large amount of data – but they also have an overall plan for the analysis phase. We have developed a systematic method including three major steps on how to manage this phase:Step 1: Create an overview of all the data (often many hours of recordings) in order to identify the sequences relevant according to the study objectives. Look through all the video recordings and at the same time outline the work practice activities in a chart: what is going on: when: which technologies are used: who is using them: and where does the activity take place. Figure The chart allows us to identify which data sequences are the most relevant according to the study objective, as a large quantity of the collected data always prove not to be important .Step 2: The data-sequences identified as relevant according to the study objective are then analyzed in depth. This includes transcribing and mapping the clinicians – as well as other relevant actors – actions. We will not go into details with this process, as other researchers have already done this ,47.Step 3: The last major step is to interpret the results from step 2. According to our experiences, clinical work practices are very hard to compare across contexts, settings and time (e.g. before and after implementation of HIT), because of the individual nature of clinicians’ work. Besides, also the patients involved are different. Therefore comparing clinical work practice should be done with great caution. We share this view with other researchers ,59-62.When interpreting video-data – as any qualitative data – it is very important for the researcher to be explicit and transparent about the study paradigm as this shows the researchers ontological position. As mentioned in the method section, the same data can be interpreted differently depending of the ontological position held by the researcher.During the history of HIT, numerous studies have focused on identifying barriers and/or factors for success in HIT development and implementation Despite an awareness on the fact that the complexity and unpredictability in clinical work practice involves a great challenge with regard to set up common success and/or failure factors, more studies claim that common themes across contries and HIT systems can be identified -66. Howewithin different health care settings – ie. at hospital wards. While employing the technique, we have simultaneously improved it. This process has provided us a thorough insight into when and where video observation is beneficial to HIT development – and when it is not. We find this a solid foundation for passing on our experiences.The views presented in this paper are based on more than ten years of scientific studies using video observation for data collection within healthcare settings –ie. at hospital wards. This permits us to explore context dependent clinical sociomaterial work practices in real time and place.So far, other researchers have only studied clinical work practice by traditional ethnographic methods or in usability laboratories or clinical set-ups. When studied in the latter, e.g. unforeseen disruptions and communication challenges are hard to imitate no matter how realistic the artificial clinical setting is set up -39. WhenAs for all other types of qualitative data collection methods and techniques involving the researcher, video observation will not lead to the “truth” about how clinical work is practiced. However, data collection using video observation has many advantages in providing an understanding of clinical work. Roughly speaking, video observation allows you a) to be a fly on the wall – record what take place - and then later discuss the activities with the clinicians or b) to follow the clinicians and encourage them to elaborate on their actions while in action – this is especially relevant if there will be no chance to discuss the video recording with them later on.It is important for us to stress that our concern - when studying clinical sociomaterial work practice from a hermeneutical point of view - is to broaden our insight and understanding of local situated work practices. In several of the cases included in this paper, we have video recorded work practices one day before- and one day after the implementation of a new health information technology Table . This prany scientific study. However, the reason why we have stressed this issue repeatedly in this paper is the fact that the greatest problems that we have experienced in our video observation studies have all been due to imprecise study objectives – or objectives being changed during the study period. In one particular case, this meant that part of our baseline data became irrelevant [We are also aware of the fact that formulating a clear and measurable objective is not only important in video observation studies, as this is crucial to relevant . Thus, itime study was performed using the time-function provided with the video technique. Bearing all the usual methodological considerations in mind, we argue, that the video observation technique has great potentials also within other study-paradigms - including the positivistic.The experiences presented in this paper are based on a hermeneutical point of view, which means that the data collection has primarily been performed with a view to understand and interpret clinical work practice. However, the video observation method has also proven beneficial within the positivistic paradigm as shown in on of our cases  the insights gained of the clinical sociomaterial work practices are realistic. Additionally, that the video data generated this way provide a solid basis for dialog between clinicians and IT-professionals and other professional groups involved in HIT development. Overall, video observation can teach us to know and to better understand the users work practice, and therefore, to know better the user requirements and needs for HIT. From a socio-technical perspective this is a precondition for the development of more successful HIT systems in the future.By employing and further developing the video observation technique, we have found, that it is superior to other ethnographic research techniques when it comes to disclosing the complexity in clinical sociomaterial work practice. We have also found that by using the video observation technique The most important lessons learned during our studies are, firstly, that a well considered methodology and clear formulated objectives are extremely important to stay focused and not miss the “red thread” when analyzing the large amount of data generated when using the video observation technique. Secondly, that the video observation technique primarily should be used for studies of specific clear identified clinical work practice within delimited clinical settings, avoiding the temptation to study a wide range of clinical work practice at different clinical settings as this results a great challenge time wise.It is our hope that this paper will inspire other researchers to embark on using video observation technique and to engage in a debate on strengths and limitations of the various data collection methods in HIT studies.aDaCHI was until 2012 entitled: V-CHI .bClinical set-ups: Laboratories equipped with hospital beds and all other clinical artefacts belonging to a hospital ward.Both authors declare that they have no competing interests.Both authors contributed to the conceptual design of this study. AMH was responsible for writing the manuscript. PB was involved in drafting the manuscript and revising it critically for important intellectual content. Both authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/12/91/prepub


This paper describes the physical structure and environmental contamination in selected hospital wards in three government hospitals in Bangladesh.The qualitative research team conducted 48 hours of observation in six wards from three Bangladeshi tertiary hospitals in 2007. They recorded environmental contamination with body secretions and excretions and medical waste and observed ward occupant handwashing and use of personal protective equipment. They recorded number of persons, number of open doors and windows, and use of fans. They measured the ward area and informally observed waste disposal outside the wards. They conducted nine focus group discussions with doctors, nurses and support staff.2 of floor space in the wards. A median of 4.9 uncovered coughs or sneezes were recorded per 10 m2 per hour per ward. Floors in the wards were soiled with saliva, spit, mucous, vomitus, feces and blood 125 times in 48 hours. Only two of the 12 patient handwashing stations had running water and none had soap. No disinfection was observed before or after using medical instruments. Used medical supplies were often discarded in open containers under the beds. Handwashing with soap was observed in only 32 of 3,373 handwashing opportunities noted during 48 hours. Mosquitoes and feral cats were commonly observed in the wards.A median of 3.7 persons were present per 10 mThe physical structure and environment of our study hospitals are conducive to the spread of infection to people in the wards. Low-cost interventions on hand hygiene and cleaning procedures for rooms and medical equipment should be developed and evaluated for their practicality and effectiveness. Hospital-acquired infection represents a major public health concern worldwide. Hospitals have played a significant role in the spread of emerging infections. In Toronto, 77% of case patients were exposed to severe acute respiratory syndrome (SARS) in hospital settings in 2003 Pathogens in hospital environments can be transmitted through airborne particles, fomites, respiratory droplets or direct contact with bodily fluids The study protocol was approved by the Ethical Review Committee of the International Centre for Diarrhoeal Disease Research, Bangladesh . The team obtained informed consent from hospital authorities for data collection and secured written consent from participants before conducting discussions. They observed public behavior and individuals were not identified.The main methods of data collection for this exploratory qualitative study were structured and semi-structured direct observation. A team of three anthropologists and two sociologists, trained in qualitative research methods, collected data from March through September 2007 from one pediatric and one adult male medicine ward from each of three public tertiary teaching hospitals. They mapped the wards to describe physical layout and calculated the floor area. Next, they conducted 48 hours of observation in 22 sessions: three to four sessions in each ward. To capture variation in activities at different times of day, sessions were held during three non-overlapping periods; three hours from 9∶00 am–2∶30 pm, three hours from 3∶30 pm–9∶00 pm and one hour from 10∶00 pm–12∶30 am. Through structured direct observation, they recorded number of ward occupants-including patients, family caregivers, visitors and healthcare workers, use of fans, and number of open doors and windows at the beginning and end of each session. They also recorded frequencies of coughing and sneezing on the wards, use of personal protective equipment and handwashing. They recorded handwashing opportunities, defined as events during which ward occupant hands were contaminated with body secretions or excretions. Handwashing opportunities included points at which hands should have been washed before an activity, such as providing patient care To complement observation findings, the team conducted nine focus group discussions; one with each of the groups of doctors, nurses and support staff in each hospital. They approached all the staff working in the study wards and some staff from other wards and enrolled those who consented to participate in the discussions. Each discussion included six to 11 participants and lasted for 45 to 80 minutes. The discussions were facilitated at the hospitals by NAR, RS and MSI and audio recorded.The team expanded the observation field notes and transcribed the recorded data verbatim from discussions. NAR and RS reviewed data from observations to identify emerging themes relevant to the study objective and summarized the data according to those themes. They also reviewed focus group discussions to identify relevant data to further cross-check and complement the observations.2  ward occupants were present per 10 mor space . Wards ior space . On a feSince the hospitals were also teaching facilities, medical students accompanied senior physicians on their rounds. On one ward, 37 students accompanied two doctors for almost an hour. Students stood nearby or sat on patient beds. Below is the description of a pediatric ward from the observation notes.The ward became crowded, even the verandas were filled with patients and caregivers. Some of the patients’ mothers had another healthy child staying in the ward. On the examination bed and nursing table, doctors and nurses quickly examined two or three patients at a time, gave medicines, injections and/or nebulizers, placed and removed canula and drew blood.2, compared to 1.3 patients and 0.2 healthcare workers. Although these hospitals had fixed visiting hours, visitors entered wards at all times. In Hospitals A and C, vendors moved from ward to ward selling tea, water or snacks or offering haircutting services.Overall, family caregivers were most the numerous on our study wards in each hospital ; a mediaOpen bowls or buckets under patient beds for disposing waste  were emptied into larger drums once daily by the hospital cleaners. A cleaner stated,“We have to carry the waste [in a bucket] from the third floor on our shoulder to dump on the ground . There is nothing [like a trolley] to help carry the bucket… Sometimes there are holes in the bucket and waste drops down on our bodies.”The team observed children crawling on the floor and playing with used syringes with needles. Only Hospital C had separate cardboard boxes beside the nursing stations to discard used sharps; it also had a functioning incinerator. Cleaners discarded waste on open grounds adjacent to the hospital building. City corporation vehicles removed waste from these grounds once a week. The team observed young boys and women collecting used syringes and saline bags from hospital grounds. They reported they planned to resell them.Feral cats were commonly observed in all wards, scavenging for food in cabinets and waste bins, climbing on patient beds and sleeping on patient bedding. The team also observed mosquitoes in the wards. All patients used mosquito nets at night in Hospitals A and C, but not in Hospital B.Many windows and doors remained fully or partially closed. Some ceiling fans remained off every day due to electrical outages. Below is a description of ventilation in a pediatric ward.All fans remained off, except the one in the nursing station, and most of the windows remained closed most of the time. Family caregivers explained that the children had colds, fever, breathing difficulty and pneumonia and that airflow was harmful to patients with such illnesses.2 per hour per ward. Only 60 coughs and 20 sneezes were covered; four coughs were covered by a cloth and the rest by the cougher’s or sneezer’s hands. No persons were observed to wash hands after coughing or sneezing. Only one family caregiver used a cloth mask while caring for the patient.The team observed a total of 6,033 coughs and 75 sneezes in 48 hours of observation; a median of 4.9 (IQR: 4.1–7.4) uncovered coughs or sneezes per 10 mThe floors, walls, grills of windows and verandas, bedrails, nursing tabletops, bedcovers, mattresses and blankets were soiled with ward occupant body secretions and excretions. The team observed floors being soiled 125 times in 48 hours. The following excerpts illustrate the soiling of surfaces.While setting a blood transfusion bag in a patient’s hand, the doctor accidentally dripped blood on the bed and floor. No staff cleaned it during observation hours.While drawing gastric fluid from a patient’s stomach, fluid dripped on the bedcover and the stain remained visible two days later when the bed with the unchanged bedcover was occupied by another patient.Cleaners swept the ward floors daily with dry brooms. Although cleaners in all hospitals reported wet mopping wards two to three times daily, the team observed daily wet mopping only in Hospital A and weekly sweeping with water in Hospital B. Cleaners also reported using disinfectant while mopping if disinfectants were available. Soiled blankets or mattresses were only shaken to remove dust after being used by one patient and then provided to another. No cleaning of window grills, bedrails, cabinets or walls was observed.The floors of the patient toilet areas were wet, slippery and soiled with body secretions and excretions and food remnants. In one hour, the team observed seven pediatric patients urinate on the floor near the entrance of the toilet area at night since there was no light inside the toilet area.Doctors and nurses used the same medical instruments, such as stethoscopes, sphygmomanometers and clinical hammers, for all patients in the ward without disinfecting them between patients. Nebulizers were used 14 times and no disinfectant was observed before or after use. A doctor explained,“The same oxygen tube is used for almost all the patients. There is only one oxygen cylinder and one mask in the ward and that mask is used for every patient.”Thermometers, used in the mouth, were partially dipped in bottles half-filled with disinfectant after each patient use.A total of 3,373 handwashing opportunities were noted during 48 hours, and occurred before or after family caregivers and healthcare workers cared for patients, after ward occupants blew their noses, coughed, sneezed, or vomited, and before ward occupants consumed food . The mosNo staff mentioned knowledge of any policy or written rule on infection control. All categories of staff reported inadequate supplies of cleaning and disinfection products, bed sheets, soap and hand sanitizer. Support staff reported,“They gave us three Harpics [a brand of toilet cleaning product] last month. We must use Harpic for professors’ handwashing station daily. There are also doctors’ handwashing stations and toilets which have to be cleaned. Two of three Harpics are used for cleaning these toilets. We have to manage cleaning the five to six patient toilets with the remaining one.”There was no autoclave in the wards and nurses reported disinfecting medical instruments by boiling, which the team also observed, or immersing in chlorine-water solution for 10 minutes. A doctor stated,“An instrument should be boiled for at least 30 minutes. We are so loaded with patients that nurses are only dipping instruments in hot water.”Nurses and support staff reported using antiseptic liquid or saline to wash hands on some occasions when soap was not available. Nurses reported using surgical masks while making patient beds, which the team never observed. Nurses also reported that they could change bed sheets only every one to two weeks due to inadequate supply. They mentioned family caregivers sometimes took linens soiled with patient body secretions and excretions to wash at home because water was often unavailable in the hospital. Support staff reported using gloves when caring for patients with infectious diseases like hepatitis B, but the team never observed this. Staff also reported that supplies could not be accessed in the evening or at night when the nurse-in-charge was not on ward duty.Overcrowding, inadequate sanitary facilities, lack of routine cleaning, lack of basic infection control measures and improper waste management combined to create numerous opportunities for transmission of infection in the study wards. This environment posed a threat of infection to all ward occupants.2 ward area 2. The uncontrolled flow of visitors in our study wards may influence prevalence of hospital-acquired infection, as was noted in a cross-sectional study in surgical wards in a Bangladeshi tertiary hospital Crowding in hospitals facilitates the spread of many diseases Staphylococcus aureus (MRSA).Hospital surfaces could be potential reservoirs of nosocomial pathogens that can survive for a few days to several months Most hospital infections are acquired via direct contact The presence of animals inside wards poses a threat of transmission of zoonotic diseases. In a geriatric care center, a cat was associated with an outbreak of epidemic MRSA Improper handling and unsafe disposal of hospital waste is a public health concern globally This study was conducted in only three public tertiary hospitals that were not randomly chosen; therefore the findings cannot be generalized to other government tertiary hospitals, private clinics or other non-government hospitals in Bangladesh. However, our findings are consistent with other studies in Bangladeshi tertiary hospitals that have reported crowding in wards Public hospitals play a crucial role in ensuring healthcare services for the poor in Bangladesh


The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail.We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations.We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management. Computational models and multi-scale numerical simulations represent essential tools in the understanding of the epidemic spread of infections, in particular to study scenarios, and to design, evaluate and compare containment strategies. The applicability of such computational studies crucially depends on informing transmission models with actual data. We are currently witnessing an important evolution as more and more data on human mobility and behavioral patterns become accessible -9. For iWhen considering smaller scales, the knowledge of contact patterns among individuals becomes relevant for identifying transmission routes, recognizing specific transmission mechanisms, and targeting groups of individuals at risk with appropriate prevention strategies or interventions such as prophylaxis or vaccination . SeveralThe simplest approach to the description of contact patterns is the homogeneous mixing assumption, which postulates that each individual has an equal probability of having contacts with any other individual in the population . A widelThe use of models with structured populations can help to define more refined analytical approaches . In addiOverall, in the context of a specific modeling problem, little is known about the level of detail that should be incorporated in modeling contact patterns. Coarse representations such as the homogeneous mixing assumption leave out crucial elements, but are analytically tractable and can provide a coarse understanding of epidemic processes. More realistic approaches are however needed when the aim is to predict with quantitative accuracy the outcomes of specific scenarios, to target groups of individuals who are most at risk, and to quantitatively evaluate and rank interventions and containment strategies according to their effectiveness. In these cases it is crucial to achieve realistic simulations to estimate quantities such as the extinction probability or the attack rate. However, very detailed representations may critically lack transparency about the specific role of the many modeling assumptions they incorporate, and might yield unnecessarily fine-grained predictions. For instance, in order to evaluate the relative efficacy of targeted vaccination of different groups of persons, we do not need to know the risk of infection of each individual — it is sufficient to estimate the average attack rate in each group.As technological advances keep enhancing our ability to gather high-resolution data on proximity and face-to-face interactions -9 and toSeveral studies have started to tackle such issues within the framework of the description of human interactions as static or dynamic contact networks, in the case of unstructured populations ,24,45-48To date, the more complex case of a structured population has not been investigated under this perspective of data summarization. Here we address this problem motivated by the needs of applications and interventions aimed at the containment of diseases, such as identifying groups of individuals that need to be prioritized in deploying containment strategies such as vaccination and prophylaxis.The aim of the present paper is therefore to understand whether and how different representations of the contact patterns within a structured population might lead, in simulation, to different estimates for the outcomes of an epidemic process and for the attack rates within groups of individuals: How faithful to the high-resolution empirical data are the summarized representations of contact patterns, in terms of the spreading process they yield and of the risk groups they identify? Given a specific epidemic process to be modeled, what is the optimal tradeoff between the compactness of the data representation and the amount of detail retained in the model? The answer to these questions lies in designing compact yet informative representations of contact patterns that can be used to model epidemics in structured communities . In such contexts, the design and evaluation of prevention measures or containment policies is naturally based on targeting specific classes of individuals. For example, it is possible to prioritize the vaccination of a certain class of individuals , whereaHere we put forward such a representation and we validate its appropriateness and usefulness in a case study of structured population: to this aim, we leverage a high-resolution dataset on the face-to-face proximity of individuals in a hospital ward, collected by the SocioPatterns collaboration by using wireless wearable sensors. The population is structured in different classes that correspond to different roles in the hospital ward. The collected data, described in detail in Ref. , includeWe consider various representations of the hospital ward data, corresponding to different types and degrees of aggregation. At the most detailed level, the raw data can be viewed as a dynamic contact network, where all the available information on the interactions between pairs of individuals (contact times and durations) is explicit. We then construct static networks that project away the temporal structure of the contacts but retain the identity of each individual and the structure of her contact network. At a coarser level, we consider the customary contact matrix representation of the data, which aggregates along the class dimension: All individuals belonging to the same role class are grouped together, and the contact rate between two individuals of given classes is given by the corresponding element of the contact matrix. This representation, by definition, discards many heterogeneities of contact behavior at the individual level as well as the heterogeneities among contacts. Between the contact matrix view and the individual-based network view, other intermediate representations are possible. Here we introduce a novel representation based on a contact matrix of distributions, designed to retain the heterogeneous properties of contact durations among pairs of individuals belonging to given role classes.We then use an SEIR process to model the spread of an infectious disease in a structured hospital population whose contact patterns are described by using all of the above representations, computed from the raw proximity data. We simulate the distribution of attack rates for the various role classes and compare these outcomes against individual-based simulations that take into account the most detailed description of the empirical contacts. Our results highlight similarities and differences in the dynamics obtained for various aggregation levels of the contact pattern representation. In particular, they show that coarse representations that do not take into account the heterogeneity of contact durations, such as the usual contact matrix, yield rather bad estimates for the global attack rate. Such representations also lead to a wrong classification of role classes in terms of their relative risk and could therefore lead to incorrect prioritization for interventions such as targeted vaccination or prophylaxis. The novel representation that we put forward is shown to strike a promising balance between simplicity and generalizability on the one hand, and on the other hand the ability to yield accurate evaluations of risk and a correct prioritization of groups of individuals in the design of targeted measures.The dataset we use  describeThe study was approved by the Ethical Committee of the Bambino Gesù Hospital . As descsame empirical data, summarized or modeled at different levels of detail . In the following we describe the contact pattern representations we will use.An analysis of the recorded contact patterns is described in Ref. . At the The representation that most closely corresponds to the raw empirical data, here forth named “DYN”, is the time-varying individual-based contact network, where for each pair of individuals the precise starting and ending times of individual contact events are available with the finest temporal resolution of 20 seconds. When simulating a process, such as the spread of an infectious disease, characterized by longer time scales than the available interval of one week, we simply play back the recorded contact history over and over. Whereas other procedures are possible for artificially extending the time span of the empirical data, it was shown in  that theThe DYN representation is thus the one that contains the most detailed information on the empirical contact patterns, and therefore we will consider it as the reference against which all the other representations have to be compared.Starting from the dynamical contact network representation (DYN), different aggregation procedures are possible that retain only selected features of the contact network while aggregating over the others. At the most aggregated level we consider, we disregard the topology of the empirical contact network as well as the contact heterogeneities and assume that each individual is in contact with all others for the same average time. We refer to this very coarse representation as “FULL”, because its contact structure is a fully connected graph, with homogeneous contact durations.As pointed out above, several intermediate representations can be designed between the fine-grained DYN representation and the coarse-grained FULL one. We define a space of possible representations by considering two qualitative features: the amount of information on the network structure, and the amount of information on contacts . Figure static and weighted contact network: individuals who have been in contact during the data collection are linked by an edge that is weighted by the total time they have spent in face-to-face proximity. All the dynamical contact events are thus summarized into a single contact graph that includes information on the observed contacts between individuals (who has met whom) and on the total (heterogeneous) duration of these contacts, i.e., how long A and B have been in contact. HET disregards information on the time of occurrence and on the temporal ordering of contacts. A less informative static network representation can be obtained from HET by removing the weight heterogeneity: this homogeneous network (HOM) is constructed by retaining the topology of the HET network, while giving all links the same weight, set as the average total duration of contacts between two individuals that have met at least once.The first two representations we consider are designed to preserve the empirical structure of the contact network (who has met whom). The first representation  as follows. We build a complete contact graph where each pair of individuals , with x in class X and y in class Y, is linked by an edge of weight wXY that depends only on the respective classes of the individuals. We set wXY = WXY/(NXNY), where NX is the number of individuals in class X, NY the number of individuals in class Y, and WXY is the total time spent in contact by any individual of class X with any individual of class Y. In the case X = Y, we set wXX = WXX/(NX(NX-1)/2)), where the denominator is the number of within-class edges. Similarly to the FULL representation, the CM representation does not preserve the topology of the empirical contact network, but the more coarse-grained contact structure between classes of individuals is retained by associating pairs of individuals with weights that depend on their classes.The role structure of a hospital ward naturally lends itself to the definition of As already mentioned above, it is known  that theContact Matrix of Distributions and define a corresponding network representation : we build a complete contact graph, where each pair of individuals , with x in class X and y in class Y, is linked by an edge whose weight wxy is a random variable obtained by sampling the negative binomial distribution with the parameters specified by the  entry of the contact matrix of distributions. A weight wxy = 0 indicates that there is no link between individuals x and y. We remark that the detailed structure of the empirical contact network is not retained in this representation, but that the average number of links with non-zero weight between each pair of classes is preserved.Similarly to the customary contact matrices, the contact matrix of distributions is not an individual-based representation. Hence, in analogy to what we have done above, we start from the XY the number of observed edges between individuals of classes X and Y, and by WXY the total time spent in contact by any individual of class X with any individual of class Y. The  entry is a bimodal distribution that assumes the value WXY/EXY  with probability EXY/(NXNY), and the value 0 with probability 1- EXY /(NXNY). On building a network representation from the matrix, for each pair of individuals , with x in class X and y in class Y, we accordingly set wxy = WXY/EXY with probability EXY/(NXNY), and wxy = 0 with probability 1- EXY /(NXNY). This representation is discussed in the Additional file Finally, we also report on an intermediate contact matrix representation designed to capture the link density and the average weights of the contact sub-network connecting classes X and Y, without taking into account the corresponding variance of edge weights. This is built as a contact matrix of bimodal distributions  that are used in place of the negative binomial distributions. As in the CMD case, the matrix is defined so that the entry for role classes X and Y consists of the parameters of a bimodal distribution that summarizes the empirical contacts between all pairs of individuals x in class X and y in class Y. Specifically, let us denote by Ev and acquire permanent immunity to the disease. The initial conditions we use correspond to a population of susceptible individuals and a single infectious individual chosen at random.To simulate the spread of an epidemic we use an SEIR model without births, deaths or introductions of individuals. In this model, individuals can be in one of 4 distinct states: susceptible (S), exposed (E), infectious (I) or recovered (R). Susceptible individuals can become exposed with a given rate β, when they are in contact with infectious individuals. Exposed individuals do not transmit the disease through contacts with others, but become infectious with a rate σ, where 1/σ is the average duration of the latent period of the disease. Infectious individuals can transmit the disease through contacts with susceptible ones. Finally, infectious individuals become recovered with rate The transitions from the exposed to the infectious state and from the infectious to the recovered state do not depend on the contact behavior of each individual. On the other hand, the probability that an infectious individual transmits the disease to a susceptible one does depend on the duration of their contacts. In order to compare the simulated spreading dynamics of an epidemic for different representations of the contact patterns we need to adequately define the rate of infection β for an infectious-susceptible pair, so that in all cases we have the same average infection probability. In particular, care must be taken in comparing time-varying network representations with static network representations such as HOM or HET. The corresponding procedure is detailed in  and in tWe consider two different scenarios for the simulation of epidemic spread: in the first scenario, the latent period is set to 2 days and the average infectious period is 1 day, whereas in the second scenario we halve these intervals in order to better probe the interplay with the fast temporal scale of human contacts. The parameters we use are:-4 s-1(short latent and infectious periods)(i). scenario 1: 1/σ = 1 day, 1/ν = 2 days and β = 6.9 10-3 s-1 (very short latent and infectious periods).(ii). scenario 2: 1/σ = 0.5 day, 1/ν = 1 day and β = 2.8 10For each scenario and each data representation , we performed 16,000 simulation runs of the epidemic spread. In the CMD and CMB cases we also performed 16,000 runs, obtained by generating 1,600 different contact network samples from the fitted contact matrices of distributions, and by simulating for each of them 10 realizations of the epidemic spread.0. This procedure is considered in the Additional file In the following we will keep the above “microscopic” epidemiological parameters fixed, and we will simulate an SEIR model over different representations of contact patterns. An alternative approach to carry out this comparison consists in tuning the model parameters so that the spread on the different contact pattern representations leads to the same basic reproductive number RThe empirical contact data are analyzed and characterized in Ref. . In ordeThe simulated properties of epidemic spread, based on different contact pattern representations, are quantified and compared by means of several indicators that we compute over an ensemble of stochastic realizations of the epidemic process. We focus in particular on the number of recovered individuals at the end of the spreading process: its distribution provides important information on the expected magnitude of the epidemics and can hence be used to evaluate the effectiveness of interventions. We study the impact on this distribution of the choice of the initial seed  and compute the distributions of attack rates for each role class, as such measures can be used to design and evaluate containment policies targeting specific roles or groups of individuals. Finally, we compute the extinction probability of the epidemics, as well as the fraction of stochastic realizations that yield a global attack rate lower than 10%.For completeness, in the Additional file In all cases, the results of simulations are compared with the spread simulated on the DYN representation, i.e., the representation that incorporates the most detailed information on the precise timing and duration of contacts. For our purposes, the faithfulness of a data representation is measured by how close the outcomes of the epidemics spread based on that representation are to those of the simulations based on the DYN representation.The dataset we use provides information on approximately 16,000 contact events, recorded during one week, among 119 individuals: 37 patients , 31 caregivers (26.1%), 20 physicians (16.8%), 21 nurses (17.6%), and 10 ward assistants (8.4%).Table Table In the following we systematically discuss the results corresponding to the second set of parameter values for the SEIR model (very short latent and infectious periods) and, when appropriate, point to differences with the results from the first parameter set, which are reported in the Additional file Figure When the epidemic reaches more than 10% of the population, the distributions of its final size, shown in Figure The probability of extinction and the fraction of stochastic runs that result in an attack rate lower than 10% Table  are muchTable Figure Finally, Figure The above comparison assumes that the epidemic parameters of the SEIR model are known and that their values are characteristic of the “microscopic” dynamics of the infectious disease, at the level of individual face-to-face contacts. As discussed in the Methods section, an alternative approach to carry out this comparison is to tune the model parameters for each case (contact pattern representation) so that all cases yield, in simulation, the same average number of secondary infections from an initially infected individual. The above comparison can then be repeated. In the Additional file We have introduced and validated in a case study a novel way of summarizing and using in simulation high-resolution contact data that describe the interactions between individuals in a structured population. We based our work on a high-resolution time-resolved dataset on the face-to-face contacts of individuals in a hospital ward. We have defined several representations of the contact data that correspond to different levels of aggregation, from the high-resolution dynamical information on all individual contacts (DYN), passing through several intermediate representation that include individual-based static networks (HOM and HET) and contact matrices , to the coarse homogeneous mixing description (FULL). In particular, we have defined a new representation in terms of a contact matrix of negative binomial distributions (CMD) that has the simplicity of a contact matrix formulation but takes into account the heterogeneity of contact durations between individuals.To compare these representations and assess their ability to correctly inform public health policies, such as targeted vaccination strategies, we used each of the representations to simulate the spread of an infectious disease, modeled by an SEIR model, and we compared the results with the outcome of simulations based on the most detailed representation (DYN), which we regard as a gold standard.average contact durations of different role pairs, discarding all the heterogeneities that are known to exist in the contact properties of individuals that belong to given role classes [The possibility to use a certain data representation depends on the data at hand, and each representation has advantages and limitations. In particular, both dynamical and heterogeneous network representations  rely on a rather large amount of information on contact patterns. Although emerging pervasive technologies allow to gather such information in diverse contexts ,5,7,8, t classes .These considerations have prompted us to move beyond the customary contact matrix representation, in particular to take into account three important features of the empirical data: 1) cumulative contact durations are known to display important temporal and role-related heterogeneities, 2) empirical contact networks are sparse, and 3) the density of links in the sub-networks restricted to given role pairs strongly depends on the specific role pair . We haveWe validated the usefulness of the proposed contact matrix representation using data from a real-world environment, namely a high-resolution record of the contacts within the structured population of a hospital ward. We described these high-resolution contact patterns using both the standard contact matrix representation and the representation we introduced here. We then used the two representation to simulate the epidemic spread within the hospital community. Each representation was evaluated by comparing the final size of the epidemics, the impact of the seed role class, and the final attack rates in each role class against simulations based on the much richer network-based representations (HET and DYN), which retain several heterogeneities and correlations of the empirical dataset. Our results show that the usual contact matrix representation leads to an overall strong overestimation of the outbreak probability and of the attack rate. Moreover, the relative risks of individuals belonging to different classes are not correctly estimated from the simulations performed with the usual contact matrix. On the other hand, the contact matrix of distributions (CMD) affords a very good qualitative and quantitative agreement with the spreading patterns obtained with the heterogeneous network representation (HET), even though the CMD representation contains significantly less information on the contact network structure than HET  network representation with heterogeneous weights (HET) yields here a smaller probability of extinction than observed for the full dynamical contact data (DYN). When restricting the comparison to outbreaks with final attack rates higher than 10%, a small but systematic difference in the number of observed cases is observed, while similar timings are obtained. These results are somewhat different from the ones obtained in Ref  for the In both cases (HET and DYN), the role class of the initial seed has a strong impact on the extinction probability and on the probability of observing a large outbreak: if the seed is a ward assistant or a nurse, the probability of a large outbreak is much larger. In addition, assistants and nurses have an overall larger risk compared to the other role classes. These results are consistent with literature that highlights the crucial importance of prioritizing nurses for local infection control interventions .In our analysis we have only considered two extreme cases of temporal aggregation of the data. The DYN representation retains the full temporal information, i.e., the precise starting and ending time of each contact event. Conversely, the static network and contact matrix representations aggregate the contact behavior of individuals over a whole week. It is clearly possible to consider intermediate temporal aggregation levels that include some information on the activity schedules, for instance at the daily scale. We have thus built daily contact networks, as well as daily contact matrices and daily contact matrices of distributions: these representations take into account the fact that not all individuals are present every day, and that the contact patterns can vary from one day to the next. The results of SEIR spreading simulations, presented in the Additional file We finally note that the study of time-resolved networks is still in its infancy . Recent We have shown in a case study that the usual contact matrix representation of empirical contact patterns in a structured population can fail to correctly inform models of epidemic spreading and of the ensuing relative risk for groups. We have introduced an alternative representation of the empirical contact data based on a contact matrix of negative binomial distributions. This representation affords a tradeoff between too coarse and too detailed representations of the data. We have shown in a specific case that it allows to correctly model important features of epidemic spread, and in particular to estimate the relative risk of individuals in different role classes, while simultaneously maintaining a compact form that retains very little information from the time-varying contact network it summarizes. This is a step in the direction pointed by Ref. , as thisSome limitations of the present study need to be discussed explicitly. First of all, the study was performed based on data for a relatively small population , taken at one point in time, and considered diseases with short latent periods. On studying the DYN case, the long but limited temporal span of the data set required the generation of artificially extended activity timelines, obtained by repeating the empirical timeline. The static representations we considered do not take into account behavioral variations across days of the week, nor the heterogeneous amount of time spent by the various individuals in the ward. It is for instance possible that larger differences between the results of the simulations on the CMD and HET representations would be observed in larger populations because of specific network effects.These limitations point to natural ways of extending the work reported here. In order to validate our approach in other contexts and for different categories of diseases, e.g. with longer latent periods, it is crucially important to measure high-resolution contact patterns in different structured populations, in larger populations, and over longer time scales. Moreover it would be important to check the robustness of the fitting procedure based on negative binomial distributions and to test other possible functional forms that can fit the empirical distribution.Other perspectives include testing data-driven containment strategies based on the various contact patterns representations and understanding how the evaluation of their performance depends on the representation, in particular for the case of the novel representation defined by the contact matrix of distributions we introduced here. A particularly interesting validation would be to extract a contact matrix of distributions from one set of data, e.g. in a hospital, to use it to design efficient containment strategies, and then to apply these strategies to a simulated epidemic on another dataset for a similar environment, for instance another hospital.The authors declare no competing interest.AB and CC designed the study. AB, CC, FG, CR, AET designed and executed the contact network measurements in the hospital. FG and AET managed the field operations, collected participant metadata and cleaned the data. AB, CC, AM analyzed the data. AM performed the simulations. AB, CC, AM wrote the manuscript. All Authors contributed to the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/13/185/prepubSupporting Text.Click here for file


For centuries after its first description by Galen, the thymus was considered as only a vestigial endocrine organ until the discovery in 1961 by Jacques FAP Miller of its essential role in the development of T (thymo-dependent) lymphocytes. A unique thymus first appeared in cartilaginous fishes some 500 million years ago, at the same time or shortly after the emergence of the adaptive (acquired) immune system. The thymus may be compared to a small brain or a computer highly specialized in the orchestration of central immunological self-tolerance. This was a necessity for the survival of species, given the potent evolutionary pressure imposed by the high risk of autotoxicity inherent in the stochastic generation of the diversity of immune cell receptors that characterize the adaptive immune response. A new paradigm of “neuroendocrine self-peptides” has been proposed, together with the definition of “neuroendocrine self.” Neuroendocrine self-peptides are secreted by thymic epithelial cells (TECs) not according to the classic model of neuroendocrine signaling, but are processed for presentation by, or in association with, the thymic major histocompatibility complex (MHC) proteins. The autoimmune regulator (AIRE) gene/protein controls the transcription of neuroendocrine genes in TECs. The presentation of self-peptides in the thymus is responsible for the clonal deletion of self-reactive T cells, which emerge during the random recombination of gene segments that encode variable parts of the T cell receptor for the antigen (TCR). At the same time, self-antigen presentation in the thymus generates regulatory T (Treg) cells that can inhibit, in the periphery, those self-reactive T cells that escaped negative selection in the thymus. Several arguments indicate that the origin of autoimmunity directed against neuroendocrine glands results primarily from a defect in the intrathymic programming of self-tolerance to neuroendocrine functions. This defect may be genetic or acquired, for example during an enteroviral infection. This novel knowledge of normal and pathologic functions of the thymus constitutes a solid basis for the development of a novel type of tolerogenic/negative self-vaccination against type 1 diabetes (T1D). Thymus vulgaris. Galen also suspected that the thymus was the seat of courage and affection because of its close vicinity to the heart; in Ancient Greece, the word “thumos or thymos (θυμóς)” indicated a physical association between breath and blood, and referred to one of Plato's three constituent parts of human psyche.Galen (129–210 AC), who, with Hippocrates, is recognized as one of the fathers of Western medicine, first observed, behind the sternum and before the cardiac area, an organ that he named “thymus” because of its resemblance to the leaf of the thyme plant The Lancet the first hypothesis connecting autoimmune diseases with histological abnormalities in the lympho-epithelial structure of the thymus  published a series of important studies describing hyperplasia of the thymus in various endocrine diseases including acromegaly and Graves' disease, and after castration Hammar, . A few yEpithelial cells of the thymic cortex  and medulla (mTECs) constitute around 85% of the thymus parenchyme. Other cells of the thymic stroma—dendritic cells (DCs) and macrophages—derive from the bone marrow. TECs arise from the endoderm of the 3rd pharyngeal pouch that, at around day 9 of embryonic development in the mouse (E9.0), contains thymic common epithelial progenitors  or forkhead box N1 (Foxn1)  Negative selection of self-reactive T cells generated during the random generation of TCR diversity due to the activity of the two recombination-activating enzymes RAG-1 and RAG-2; (2) Generation of self-specific tTreg cells; and (3) Positive selection of CD4+ and CD8+ effector and self-tolerant T cells. The first two events establish the establishment of the central arm of self-tolerance, while the avidity-affinity of the TCR—self-antigen—MHC interaction determines T cell negative or positive selection  of neurohypophysial receptors expressed by pre-T cells, the physico-chemical conditions are conducive to a functional signaling within the thymus network, which is not the case for the blood-borne OT and AVP, due to their low plasma concentration (10−11–10−12M).At the beginning of the 20th century, English scientists reported the galactogogue activity of thymus and corpus luteum extracts after their injection into the goat . In pre-T cells, OT also phosphorylates paxillin, a 68-kD protein located at focal adhesion sites and associated with p125FAK  and a MHC class I heavy chain-related domain (45 kD). The formation of this hybrid could reside at the post-transcriptional level (such as a trans-splicing event) or at the post-translational level (such as ATP-dependent ubiquitinylation). The MHC class I domain could be implicated in the membrane targeting of the hybrid, whereas the neurophysin domain would bind OT for final presentation to pre-T cells. According to this explanation, the neurophysin part of the OT precursor could fulfill the same function in the thymus and in the hypothalamo-neurohypophysial axis: binding of the peptide OT and transport to the external limit of TECs or magnocellular neurons, respectively. The tyrosine residue in position 2 of OT plays an important role in its binding to neurophysin  and leukemia inhibitory factor (LIF) in the supernatant of human TEC primary cultures . Primary cultures of human TECs contain around 5 ng of NT/10“neuroendocrine self” could be defined according to the following features:At this point and in association with the characterization of the thymic peptides related to different neuroendocrine families —but not substance P—is the peptide generated from the processing in TECs of the preprotachykinin A gene product  controls the degree of intrathymic transcription of the genes encoding neuroendocrine self-peptides  and professional APCs  in the periphery. For some neuroendocrine self-peptides (OT and NT), such differences imply that their presentation in the thymus is not tightly restricted by MHC alleles as much as presentation of nonself antigens and autoantigens by dedicated APCs in the periphery.For some precursors of neuroendocrine self-peptide precursors, their transcription in TECs precedes their orthotopic expression in peripheral neuroendocrine glands/cells  and T-cell receptors . This extreme diversity of antigen receptors was directly associated with a high inherent risk of autoxicity that threatened survival of both species and individuals. This evolutionary pressure was so strong that, in accordance with Paul Ehrlich's concept of horror autotoxicus, novel structures, and mechanisms appeared with the specific role of protecting self against potential autoimmune attacks and orchestrating immunological self-tolerance. The first unique thymus also appeared in jawed cartilaginous fishes, concomitantly or very shortly after the emergence of adaptive immunity. The thymus did not abruptly appear but, as elegantly demonstrated by Bajoghli et al. (Foxn4), the ortholog of Foxn1, the transcription factor responsible for the differentiation of TECs in higher vertebrates as discussed above. Foxn1 thus stands at a crucial place in the emergence of thymus epithelium that is essential for the control of T cell differentiation and self-tolerance programming , mediated by diverse variable lymphocyte receptors (VLRs), with 4–12 leucine-rich repeat modules assembled by some gene conversion process. Some 500 million years ago, the emergence of transposon-like genes coding for recombination-activating enzymes RAG-1 and RAG-2 in cartilaginous fishes  initiated the development of adaptive immunity .The hierarchic organization of the thymic repertoire of neuroendocrine self-peptides is also very significant from an evolutionary point of view. Because major neuroendocrine principles had been established before the emergence of the anticipatory adaptive immune response, they had to be protected from the risk of autoimmunity inherent to this immune lottery. OT is a hypothalamic nonapeptide that is closely implicated at different steps of the reproductive process, from social affiliation and bonding to control of parturition and lactation. Thus, OT is fundamental for the preservation of animal and human species. Through its dominant expression in TECs, OT is much more tolerated than AVP, its hypothalamo-neurohypohysial homolog, which mainly controls water homeostasis and vascular tone. Interestingly, rare cases of autoimmune hypothalamitis with AVP deficiency and diabetes insipidus have been repeatedly observed  and to the absence of central tolerance to islet β cells  alleles, the second genetic trait (IDDM2) of T1D susceptibility , Ins2 is predominantly transcribed in rare subsets of mTECs while Ins1 is dominantly expressed in murine islet β cells, which leads to a higher immunological tolerance to Ins2. This difference in the topography of Ins2 and Ins1 expression explains why breeding of Ins2−/− mice onto the NOD background markedly accelerates insulitis and diabetes onset insulin (AIRE) gene, a member of the zinc-finger gene family, led to an extremely important advancement in our knowledge of the central role played by a thymus dysfunction in the pathogenesis of organ-specific autoimmune diseases  or Autoimmune Poly-Endocrinopathy, Candidiasis, Ectodermal Dystrophy (APECED) syndrome. AIRE encodes a protein with structural characteristics of a transcription factor: its transcription is maximal in thymus epithelium, and Aire−/− mice develop several autoimmune processes associated with a marked decrease in the intrathymic expression of numerous neuroendocrine self-peptides  and the level of AIRE transcription in the thymus determine the concentration of INS transcripts in the human thymus  could induce a thymus dysfunction and an impairment of central tolerance to the insulin protein/gene family. CVB4 can directly infect the epithelial and lymphoid compartments of the human and murine thymus, and promote a severe thymus dysfunction with massive pre-T cell depletion and a marked-up regulation of MHC class I expression by TECs and by double positive CD4+CD8+ immature thymic T cells , are also transcribed in human TECs in normal conditions insulin has an essential role in protecting islet β cells against diabetogenic autoimmunity. However, insulin per se does not exert any significant tolerogenic properties that could be exploited to reprogram immunological tolerance to islet β cells. With the exception of two studies (Fourlanos et al., Although in vivo and to prevent autoimmune diabetes in NOD mice (Daniel et al., Igf2 transcription is defective in the thymus of BBDP rats (Kecha-Kamoun et al., Nevertheless, the development of peptide-based therapeutic vaccines remains an attractive approach because of the specificity of immune suppression or regulation directed to specific pathogenic self-reactive T cells. In this context, infusion of a strongly active insulin mimetope was recently shown to convert naïve T cells into Foxp3+ Treg cells Our studies have established that the thymus is not a classical neuroendocrine gland, but an obligatory intersection between the adaptive immune and neuroendocrine systems Geenen, . MoreoveThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Purpose: To evaluate the imaging appearance of the thymus in the myasthenic patients by using chemical shift magnetic resonance imaging, and, to correlate the chemical shift ratio (CSR) with pathologic findings after surgical excision.Materials and Methods: In the past year, a total of 11 myasthenic patients , have been investigated by MRI centered at the thymic lodge. Our protocol included a Dual-Echo technique, T1-weighted In-phase/Opposed-phase MR images in all patients. A chemical shift ratio (CSR) was calculated by comparing the signal intensity of the thymus gland with that of the chest wall muscle for quantitative analysis. For this purpose, we have used standard region-of-interest electronic cursors at a slice level of the maximum axial surface of the thymus. We have identified two patients groups: a thymic hyperplasia group and a thymic tumoral group.Results: With the decrease in the signal intensity of the thymus gland at chemical shift, the MR imaging was evident only in the hyperplasia group. The mean CSR in the hyperplasia group was considerably lower than that in the tumor group, 0,4964 ± 0,1841, compared with 1,0398 ± 0,0244. The difference in CSR between the hyperplasia and tumor groups was statistically significant .Conclusion: MR imaging using T1-weighted In-phase/Opposed-phase images could be a useful diagnostic tool in the preoperative assessment of the thymic lodge and may help differentiate thymic hyperplasia from tumors of the thymus glandAbbreviations: myasthenia gravis – MG; chemical shift ratio – CSR; frequency-encoding direction – FED The examination of the thymus gland is usually done by computed tomography (CT). In the Department of Radiology and Medical Imaging of “Fundeni” Clinical Institute, this type of examination is performed mainly in patients with myasthenia gravis (MG), admitted in the Department of Neurology and in the Department of General Surgery and Liver Transplantation, with an experience of 30 years regarding CT examinations.Sometimes, the CT appearance of the thymus, correlated with the patient's age do not allow a specific diagnosis, such as diffuse increase in size of the thymus, this appearance being seen in thymic hyperplasia, thymomas and reactive changes of the thymus frequently associated with chemotherapy, radiotherapy, corticosteroid therapy, and severe burns.1]. Minimal invasive surgical techniques including transcervical incision and video-assisted thoracoscopic surgery and robotic-assisted thymectomy are not recommended in patients with thymic malignancies and high risk of local recurrence. Therefore, the importance of preoperative evaluation of the thymic abnormalities has increased as more therapeutic techniques have been developed [2].Chemical shift MR imaging with Dual-Echo sequences In-phase/Opposed-phase proved extremely useful in the diagnosis of the lesions with substantial fatty elements. Chemical Shift imaging can aid in the diagnosis of lipid-containing lesions of the brain  or the body such as adrenal adenoma, focal fat within the liver, and angiomyolipoma .When an external magnetic field is applied to the tissue, its nuclei will resonate at a specific frequency that depends on the strength of that external magnetic field (B0) and its local microenvironment. The effects of the external magnetic field can be determined by the Larmor equation: ω0 = γ • B0, where ω is the resonant or precessional frequency and γ is the gyromagnetic ratio for a specific nuclear species [2].The Chemical Shift is a consequence of the variations in resonance frequency (Larmor frequency) of the protons located in different chemical environments. The effects of the local environment on the individual precessional frequency of each nucleus are further influenced by the electron shell interactions of each nucleus with those of surrounding molecules [2].In clinical imaging, hydrogen protons form the basis for MR image signal and contrast. The chemical shift phenomenon is the most noticeable between the hydrogen protons of lipid and those of water, because of their large relative differences in magnetic shielding. The difference in resonant frequencies between lipid and water protons increases proportionately with the static magnetic field strength [3].The approximate chemical shift between human lipid and water is of 3.5 parts per million. When taking into account a field strength of 1.5 T, this shift translates to approximately a 224-Hz separation between the resonant frequencies of fat and water protons [4]. This causes misregistration of the position of lipid proton voxels along the frequency-encoding direction (FED) with a positional shift toward the direction of decreasing frequencies. At a planar lipid-water interface, oriented perpendicularly to the FED, this positional misregistration can cause two effects. A linear band of pixels with decreased signal intensity occurs at the interface, when lipid is on the low-frequency side of the FED (with respect to water). This results from the lipid pixels that are shifted away from the water pixels, which leaves pixels on the lipid side of the interface with absent signal. A linear band of pixels with increased signal intensity occurs at the interface when lipid is on the high frequency side of the FED (with respect to water). This results from the lipid pixels being shifted toward the water pixels, which causes pixels on the waterside of the interface to have signal intensity equal to the sum of the lipid- and water-containing pixels [3]. This corresponds to all frequencies contained in the signal, applying a gradient during signal reception, leading to frequency dispersion. Depending on the number of samples in the direction of frequency encoding (or the number of pixels in this direction) and the time of observation corresponds to the period of time the read gradient is applied.The chemical shift also depends on sampling the bandwidth. The distance of the chemical shift artifact is inversely proportional to the sampling bandwidth .The signal intensity of the thymus gland was compared with that of the chest-wall muscle : CSR = (tSIop/mSIop)/(tSIin/mSIin) .The chemical shift ratio (CSR), which was determined by comparing the signal intensity of the thymus gland (tSI) with that of the chest-wall muscle (mSI), both in phase (in) and opposed-phase (op) images, was calculated by using the following equation . It can be challenging to differentiate the hyperplastic thymus from thymoma based on the morphologic assessment alone, especially in patients with MG [13].Sometimes, thymoma may show diffuse thymic enlargement at CT, and this can be incorrectly diagnosed as a hyperplastic thymus; in addition, hyperplastic thymus may appear at CT as a soft-tissue mass mimicking thymoma [14]. In patients with thymoma, the MG is present or appears in 30-50% of the cases [15]. Lymphoid thymic hyperplasia is most commonly associated with MG, being seen in up to 65% of the cases [15]. Minimal invasive surgical techniques are not recommended in patients with thymic malignancies with high risk of local recurrence [1].Currently, the thymus is surgically resected in patients with MG because at least an improvement of clinical symptoms due to MG can be obtained, even if there is no associated thymoma [Chemical shift MR imaging can help differentiate between hyperplastic thymus and thymoma according to our study, based on the assessment of the CSR. CSR allows us to perform a quantitative assessment. The signal intensity of the thymus gland was compared with that of the chest-wall muscle on both in-phase and opposed-phase images.All the patients in the hyperplasia group showed a homogeneous decrease in the signal intensity of the thymus gland on the opposed-phase image relative to the in-phase image. None of the patients in the tumor group showed a decrease in the signal intensity of the thymus gland at the chemical shift MR imaging. In addition, the mean CSR in the hyperplasia group was considerably lower than that in the tumor group, and there was no overlap in range.We found recent data in literature that prove the usefulness of the chemical shift MR imaging to identify normal and hyperplastic thymus, by proving that normal fat infiltration and the differentiation of thymic hyperplasia from tumors of the thymus gland in patients with different pathological entities. Our study included only patients with myasthenia gravis, but an important limit of this study was the small number of patients.A larger number of patients is necessary to clarify the utility of the chemical shift MR imaging for differentiating thymic hyperplasia from tumors of the thymus gland, in myasthenic patients.Occasionally, in the first and second decades of life, when the thymus gland has not been completely replaced by fat tissue, the CT attenuation values and signal intensities on MR imaging show what a normal or pathological thymus can overlap.By using a qualitative comparison and by calculating the chemical-shift ratio between in-phase and opposed-phase gradient-echo images, particularly in patients with myasthenia gravis, it is possible to differentiate the hyperplastic thymus from the tumors of the thymus gland.