Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections

Using contact network dynamics to implement efficient interventions against pathogen spread in hospital settings: A modelling study

BACKGROUND

Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation.

METHODS AND FINDINGS

Using an individual-based model (IBM), we reproduced methicillin-resistant Staphylococcus aureus colonisation transmission dynamics over a detailed contact network recorded within a French LTCF of 327 patients and 263 staff over 3 months. Simulated baseline cumulative colonisation incidence was 21 patients (prediction interval: 11, 31) and 35 staff (prediction interval: 19, 54). We examined the potential impact of 3 types of interventions against transmission (reallocation reducing the number of unique contacts per staff, reinforced contact precautions, and hypothetical vaccination protecting against acquisition), targeted towards specific populations. All 3 interventions were effective when applied to all nurses or healthcare assistants (median reduction in MRSA colonisation incidence up to 35%), but the benefit did not exceed 8% when targeting any other single staff category. We identified "supercontactor" individuals with most contacts ("frequency-based," overrepresented among nurses, porters, and rehabilitation staff) or with the longest cumulative time spent in contact ("duration-based," overrepresented among healthcare assistants and patients in elderly care or persistent vegetative state (PVS)). Targeting supercontactors enhanced interventions against pathogen spread in the LTCF. With contact precautions, targeting frequency-based staff supercontactors led to the highest incidence reduction (20%, 95% CI: 19, 21). Vaccinating a mix of frequency- and duration-based staff supercontactors led to a higher reduction (23%, 95% CI: 22, 24) than all other approaches. Although based on data from a single LTCF, when varying epidemiological parameters to extend to other pathogens, our results suggest that targeting supercontactors is always the most effective strategy, indicating this approach could be applied to prevent transmission of other nosocomial pathogens.

CONCLUSIONS

By characterising the contact structure in hospital settings and identifying the categories of staff and patients more likely to be supercontactors, with either more or longer contacts than others, interventions against nosocomial spread could be more effective. We find that the most efficient implementation strategy depends on the intervention (reallocation, contact precautions, vaccination) and target population (staff, patients, supercontactors). Importantly, both staff and patients may be supercontactors, highlighting the importance of including patients in measures to prevent pathogen transmission in LTCF.

An algorithm to build synthetic temporal contact networks based on close-proximity interactions data

Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.

Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic

To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.

The effective reproductive number: Modeling and prediction with application to the multi-wave Covid-19 pandemic

Classical compartmental models of infectious disease assume that spread occurs through a homogeneous population. This produces poor fits to real data, because individuals vary in their number of epidemiologically-relevant contacts, and hence in their ability to transmit disease. In particular, network theory suggests that super-spreading events tend to happen more often at the beginning of an epidemic, which is inconsistent with the homogeneity assumption. In this paper we argue that a flexible decay shape for the effective reproductive number (Rt) indexed by the susceptible fraction (St) is a theory-informed modeling choice, which better captures the progression of disease incidence over human populations. This, in turn, produces better retrospective fits, as well as more accurate prospective predictions of observed epidemic curves. We extend this framework to fit multi-wave epidemics, and to accommodate public health restrictions on mobility. We demonstrate the performance of this model by doing a prediction study over two years of the SARS-CoV2 pandemic.

Network approaches and interventions in healthcare settings: A systematic scoping review

INTRODUCTION

The growing interest in networks of interactions is sustained by the conviction that they can be leveraged to improve the quality and efficiency of healthcare delivery systems. Evidence in support of this conviction, however, is mostly based on descriptive studies. Systematic evaluation of the outcomes of network interventions in healthcare settings is still wanting. Despite the proliferation of studies based on Social Network Analysis (SNA) tools and techniques, we still know little about how intervention programs aimed at altering existing patterns of social interaction among healthcare providers affect the quality of service delivery. We update and extend prior reviews by providing a comprehensive assessment of available evidence.

METHODS AND FINDINGS

We searched eight databases to identify papers using SNA in healthcare settings published between 1st January 2010 and 1st May 2022. We followed Chambers et al.'s (2012) approach, using a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. We distinguished between studies relying on SNA as part of an intervention program, and studies using SNA for descriptive purposes only. We further distinguished studies recommending a possible SNA-based intervention. We restricted our focus on SNA performed on networks among healthcare professionals (e.g., doctors, nurses, etc.) in any healthcare setting (e.g., hospitals, primary care, etc.). Our final review included 102 papers. The majority of the papers used SNA for descriptive purposes only. Only four studies adopted SNA as an intervention tool, and measured outcome variables.

CONCLUSIONS

We found little evidence for SNA-based intervention programs in healthcare settings. We discuss the reasons and challenges, and identify the main component elements of a network intervention plan. Future research should seek to evaluate the long-term role of SNA in changing practices, policies and behaviors, and provide evidence of how these changes affect patients and the quality of service delivery.

Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study

BACKGROUND

Traditionally, hand hygiene (HH) interventions do not identify the observed healthcare workers (HWCs) and therefore, reflect HH compliance only at population level. Intensive care units (ICUs) in seven European hospitals participating in the "Prevention of Hospital Infections by Intervention and Training" (PROHIBIT) study provided individual HH compliance levels. We analysed these to understand the determinants and dynamics of individual change in relation to the overall intervention effect.

METHODS

We included HCWs who contributed at least two observation sessions before and after intervention. Improving, non-changing, and worsening HCWs were defined with a threshold of 20% compliance change. We used multivariable linear regression and spearman's rank correlation to estimate determinants for the individual response to the intervention and correlation to overall change. Swarm graphs visualized ICU-specific patterns.

RESULTS

In total 280 HCWs contributed 17,748 HH opportunities during 2677 observation sessions. Overall, pooled HH compliance increased from 43.1 to 58.7%. The proportion of improving HCWs ranged from 33 to 95% among ICUs. The median HH increase per improving HCW ranged from 16 to 34 percentage points. ICU wide improvement correlated significantly with both the proportion of improving HCWs (ρ = 0.82 [95% CI 0.18-0.97], and their median HH increase (ρ = 0.79 [0.08-0.97]). Multilevel regression demonstrated that individual improvement was significantly associated with nurse profession, lower activity index, higher nurse-to-patient ratio, and lower baseline compliance.

CONCLUSIONS

Both the proportion of improving HCWs and their median individual improvement differed substantially among ICUs but correlated with the ICUs' overall HH improvement. With comparable overall means the range in individual HH varied considerably between some hospitals, implying different transmission risks. Greater insight into improvement dynamics might help to design more effective HH interventions in the future.

Use of Contact Networks to Estimate Potential Pathogen Risk Exposure in Hospitals

IMPORTANCE

Person-to-person contact is important for the transmission of health care-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.

OBJECTIVE

To generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.

DESIGN, SETTING, AND PARTICIPANTS

In this quality improvement study, mixing matrices were created using a weighted contact network of connected hospital patients, in which contact was defined as occupying the same hospital unit for 1 day. Participants included hospitalized patients at 299 hospital units in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network between January 2015 and December 2017. Analysis was conducted between October 2021 and February 2022.

MAIN OUTCOMES AND MEASURES

The mixing matrices of patients for each hospital unit were assessed using age, Elixhauser Score, and a measure of antibiotic exposure.

RESULTS

Among 1 549 413 hospitalized patients (median [IQR] age, 44 [26-63] years; 883 580 [56.3%] women) in 299 hospital units, some units had highly similar patterns across multiple hospitals, although the number of patients varied to a great extent. For most of the adult inpatient units, frequent mixing was observed for older adult groups, while outpatient units (eg, emergency departments and behavioral health units) showed mixing between different age groups. Most units mixing patterns followed the marginal distribution of age; however, patients aged 90 years or older with longer lengths of stay created a secondary peak in some medical wards. From the mixing matrices by Elixhauser Score, mixing between patients with relatively higher comorbidity index was observed in intensive care units. Mixing matrices by antibiotic spectrum, a 4-point scale based on priority for antibiotic stewardship programs, resulted in 6 major distinct patterns owing to the variation of the type of antibiotics used in different units, namely those dominated by a single antibiotic spectrum (narrow, broad, or extended), 1 pattern spanning all antibiotic spectrum types and 2 forms of narrow- and extended-spectrum dominant exposure patterns (an emergency room where patients were exposed to one type of antibiotic or the other and a pediatric ward where patients were exposed to both types).

CONCLUSIONS AND RELEVANCE

This quality improvement study found that the mixing patterns of patients both within and between hospitals followed broadly expected patterns, although with a considerable amount of heterogeneity. These patterns could be used to inform mathematical models of health care-associated infections, assess the appropriateness of both models and policies for smaller community hospitals, and provide baseline information for the design of interventions that rely on altering patient contact patterns, such as practices for transferring patients within hospitals.

Smart Building Technologies in Response to COVID-19

The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology.

Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology

BACKGROUND

Hand hygiene is one of the most effective ways of preventing health care-associated infections and reducing their transmission. Owing to recent advances in sensing technologies, electronic hand hygiene monitoring systems have been integrated into the daily routines of health care workers to measure their hand hygiene compliance and quality.

OBJECTIVE

This review aims to summarize the latest technologies adopted in electronic hand hygiene monitoring systems and discuss the capabilities and limitations of these systems.

METHODS

A systematic search of PubMed, ACM Digital Library, and IEEE Xplore Digital Library was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were initially screened and assessed independently by the 2 authors, and disagreements between them were further summarized and resolved by discussion with the senior author.

RESULTS

In total, 1035 publications were retrieved by the search queries; of the 1035 papers, 89 (8.60%) fulfilled the eligibility criteria and were retained for review. In summary, 73 studies used electronic monitoring systems to monitor hand hygiene compliance, including application-assisted direct observation (5/73, 7%), camera-assisted observation (10/73, 14%), sensor-assisted observation (29/73, 40%), and real-time locating system (32/73, 44%). A total of 21 studies evaluated hand hygiene quality, consisting of compliance with the World Health Organization 6-step hand hygiene techniques (14/21, 67%) and surface coverage or illumination reduction of fluorescent substances (7/21, 33%).

CONCLUSIONS

Electronic hand hygiene monitoring systems face issues of accuracy, data integration, privacy and confidentiality, usability, associated costs, and infrastructure improvements. Moreover, this review found that standardized measurement tools to evaluate system performance are lacking; thus, future research is needed to establish standardized metrics to measure system performance differences among electronic hand hygiene monitoring systems. Furthermore, with sensing technologies and algorithms continually advancing, more research is needed on their implementation to improve system performance and address other hand hygiene-related issues.

Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

Introduction

The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between "social distancing," a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic.

Methods

There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns.

Results

The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Patterns of patients’ transfer between different hospitals contribute crucially to the risk of hospital-acquired infections (HAIs) in the health care system. To quantify this risk, network models can be applied. The estimated risk can be inaccurate in the case of incomplete data on hospital admissions, which can be a consequence of the multiplicity of insurance companies as it is the case in Germany. To develop a better understanding of how incompleteness of data affects network measures and the simulated spread of HAI, we compared those measures derived from sampled, scale-up and original data, based on hospitalization data from three AOK insurance companies. We found that common network measures were affected by incompleteness, but the simulated prevalence as a measure of epidemic spread in the network was robust over a large range of incompleteness proportions. Epidemics and the transition of the infectious diseases may be modelled on hospital data with a coverage as low as 10% of the local population, whilst maintaining accuracy to within 10% of the true population prevalence.

Reorganization of nurse scheduling reduces the risk of healthcare associated infections

Efficient prevention and control of healthcare associated infections (HAIs) is still an open problem. Using contact data from wearable sensors at a short-stay geriatric ward, we propose a proof-of-concept modeling study that reorganizes nurse schedules for efficient infection control. This strategy switches and reassigns nurses’ tasks through the optimization of shift timelines, while respecting feasibility constraints and satisfying patient-care requirements. Through a Susceptible-Colonized-Susceptible transmission model, we found that schedules reorganization reduced HAI risk by 27% (95% confidence interval [24, 29]%) while preserving timeliness, number, and duration of contacts. More than 30% nurse-nurse contacts should be avoided to achieve an equivalent reduction through simple contact removal. Nurse scheduling can be reorganized to break potential chains of transmission and substantially limit HAI risk, while ensuring the timeliness and quality of healthcare services. This calls for including optimization of nurse scheduling practices in programs for infection control in hospitals.

A spatiotemporal simulation study on the transmission of harmful microorganisms through connected healthcare workers in a hospital ward setting

Background

Hand transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against these transmissions is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low and vary over space and time. The spatiotemporal effects on hand transmission and spread of these microorganisms for varying hand hygiene compliance levels are unknown. This study aims to (1) identify a healthcare worker occupancy group of potential super-spreaders and (2) quantify spatiotemporal effects on the hand transmission and spread of harmful microorganisms for varying levels of hand hygiene compliance caused by this group.

Methods

Spatiotemporal data were collected in a hospital ward of an academic hospital using radio frequency identification technology for 7 days. A potential super-spreader healthcare worker occupation group was identified using the frequency identification sensors’ contact data. The effects of five probability distributions of hand hygiene compliance and three harmful microorganism transmission rates were simulated using a dynamic agent-based simulation model. The effects of initial simulation assumptions on the simulation results were quantified using five risk outcomes.

Results

Nurses, doctors and patients are together responsible for 81.13% of all contacts. Nurses made up 70.68% of all contacts, which is more than five times that of doctors (10.44%). This identifies nurses as the potential super-spreader healthcare worker occupation group. For initial simulation conditions of extreme lack of hand hygiene compliance (5%) and high transmission rates (5% per contact moment), a colonised nurse can transfer microbes to three of the 17 healthcare worker or patients encountered during the 98.4 min of visiting 23 rooms while colonised. The harmful microorganism transmission potential for nurses is higher during weeknights (5 pm – 7 am) and weekends as compared to weekdays (7 am – 5 pm).

Conclusion

Spatiotemporal behaviour and social mixing patterns of healthcare can change the expected number of hand transmissions and spread of harmful microorganisms by super-spreaders in a closed healthcare setting. These insights can be used to evaluate spatiotemporal safety behaviours and develop infection prevention and control strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05954-7.

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user's indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers' length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user's location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user's locations in a complex building without complex calibration as compared to other recent methods.

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

The Role of Healthcare Worker-Mediated Contact Networks in the Transmission of Vancomycin-Resistant Enterococci

BACKGROUND

User- and time-stamped data from hospital electronic health records (EHRs) present opportunities to evaluate how healthcare worker (HCW)-mediated contact networks impact transmission of multidrug-resistant pathogens, such as vancomycin-resistant enterococci (VRE).

METHODS

This is a retrospective analysis of incident acquisitions of VRE between July 1, 2016 and June 30, 2018. Clinical and demographic patient data were extracted from the hospital EHR system, including all recorded HCW contacts with patients. Contacts by an HCW with 2 different patients within 1 hour was considered a "connection". Incident VRE acquisition was determined by positive clinical or surveillance cultures collected ≥72 hours after a negative surveillance culture.

RESULTS

There were 2952 hospitalizations by 2364 patients who had ≥2 VRE surveillance swabs, 112 (4.7%) patients of which had incident nosocomial acquisitions. Patients had a median of 24 (interquartile range [IQR], 18-33) recorded HCW contacts per day, 9 (IQR, 5-16) of which, or approximately 40%, were connections that occurred <1 hour after another patient contact. Patients that acquired VRE had a higher average number of daily connections to VRE-positive patients (3.1 [standard deviation {SD}, 2.4] versus 2.0 [SD, 2.1]). Controlling for other risk factors, connection to a VRE-positive patient was associated with increased odds of acquiring VRE (odds ratio, 1.64; 95% confidence interval, 1.39-1.92).

CONCLUSIONS

We demonstrated that EHR data can be used to quantify the impact of HCW-mediated patient connections on transmission of VRE in the hospital. Defining incident acquisition risk of multidrug-resistant organisms through HCWs connections from EHR data in real-time may aid implementation and evaluation of interventions to contain their spread.

Infection Spread and High-Resolution Detection of Close Contact Behaviors

Knowledge of human behaviors is important for improving indoor-environment design, building-energy efficiency, and productivity, and for studies of infection spread. However, such data are lacking. In this study, we designed a device for detecting and recording, second by second, the 3D indoor positioning and head and body motions of each graduate student in an office. From more than 400 person hours of data. Students spent 92.2%, 4.1%, 2.9%, and 0.8% of their time in their own office cubicles, other office cubicles, aisles, and areas near public facilities, respectively. They spent 9.7% of time in close contact, and each student averagely had 4.0 close contacts/h. Students spent long time on close contact in the office which may lead to high infection risk. The average interpersonal distance during close contact was 0.81 m. When sitting, students preferred small relative face orientation angle. Pairs of standing students preferred a face-to-face orientation during close contact which means this pattern had a lower infection risk via close contact. Probability of close contact decreased exponentially with the increasing distance between two students' cubicles. Data on human behaviour during close contact is helpful for infection risk analysis and infection control and prevention.

Spatiotemporal clustering of in-hospital Clostridioides difficile infection

OBJECTIVE

To determine whether Clostridioides difficile infection (CDI) exhibits spatiotemporal interaction and clustering.

DESIGN

Retrospective observational study.

SETTING

The University of Iowa Hospitals and Clinics.

PATIENTS

This study included 1,963 CDI cases, January 2005 through December 2011.

METHODS

We extracted location and time information for each case and ran the Knox, Mantel, and mean and maximum component size tests for time thresholds (T = 7, 14, and 21 days) and distance thresholds (D = 2, 3, 4, and 5 units; 1 unit = 5-6 m). All tests were implemented using Monte Carlo simulations, and random CDI cases were constructed by randomly permuting times of CDI cases 20,000 times. As a counterfactual, we repeated all tests on 790 aspiration pneumonia cases because aspiration pneumonia is a complication without environmental factors.

RESULTS

Results from the Knox test and mean component size test rejected the null hypothesis of no spatiotemporal interaction (P < .0001), for all values of T and D. Results from the Mantel test also rejected the hypothesis of no spatiotemporal interaction (P < .0003). The same tests showed no such effects for aspiration pneumonia. Our results from the maximum component size tests showed similar trends, but they were not consistently significant, possibly because CDI outbreaks attributable to the environment were relatively small.

CONCLUSION

Our results clearly show spatiotemporal interaction and clustering among CDI cases and none whatsoever for aspiration pneumonia cases. These results strongly suggest that environmental factors play a role in the onset of some CDI cases. However, our results are not inconsistent with the possibility that many genetically unrelated CDI cases occurred during the study period.

Analysis of network interventions with an application to hospital-acquired infections

Regional interventions to prevent the spread of hospital-acquired infections, vaccination campaigns, and information dissemination strategies are examples of treatment interventions applied to members of a network with the intent of effecting a network-wide change. In designing clinical trials or determining policy changes, it may not be cost effective or otherwise possible to treat all actors of a network. There is a notable lack of study designs and statistical frameworks with which to plan a network-wide intervention in this context and analyze the resulting data. This paper builds off of the network autocorrelation model in order to provide such a framework for a pre-post study design. We derive key quantitative measures of the network-wide treatment effect, exact formulas for power analyses of these measures, and extensions for the context in which the network is unknown. As the treatment assignation is part of the network-wide treatment, we provide methods for determining the assignation which optimizes the overall treatment effect over all members of the network subject to any arbitrary set of implementation costs and cost constraint. We implement these methods on Clostridioides difficile data for the state of California, where the hospitals are linked through patient sharing.

Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission.

Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week.

Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

Human behavior during close contact in a graduate student office

Close contact is a part of daily life, and proximity is known to play a primary role in the transmission of many respiratory infections. However, there are no data on close contact parameters such as movement of the head/body and relative location, which can affect both expiration and inspiration flows. Using video cameras, we collected such data for nearly 63 000 seconds of total close contact duration in a graduate student office in Beijing, China. Each student had on average 9.6 close contacts per hour and spent 9.9% of their time participating in close contact interactions. Males made more body/head movements than females during close contact. The probability distribution of interpersonal distance follows a log-normal distribution. The average interpersonal distance was 0.67 m. Students preferred a relative face orientation angle between 15° and 45°. When the relative face orientation angle increased, the interpersonal distance increased. Students had a high probability (73%-97%) of maintaining their head, body, and relative position during close contact, while the probability of body/head or relative position changing from any location/angle to another is also given. These data may be used for assessment of infection risk via close contact in crowded indoor environments.

Current issues in hand hygiene

Although substantial improvements in hand hygiene practices have occurred in recent years, many health care facilities continue to encounter challenges in achieving and maintaining high levels of hand hygiene compliance. Issues of current interest include the optimum dose of alcohol-based handrub (ABHR) that should be applied, the impact of hand size and alcohol-based handrub dry times have on efficacy, and ideal hand hygiene technique. There is a need to determine which additional promotional activities can augment improvements in hand hygiene that are achieved by implementing the multimodal improvement strategy recommended by the World Health Organization. Monitoring hand hygiene performance and providing personnel with feedback on their performance are essential elements of successful improvement programs. Further research is needed to establish the most effective methods of providing feedback. Additional studies are needed to optimize strategies for performing direct observation of hand hygiene compliance, and to determine the role of supplementing direct observations using automated monitoring systems.

Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

Estimating the Attributable Disease Burden and Effects of Interhospital Patient Sharing on Clostridium difficile Infections

Objective:

To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels.

Design:

Retrospective observational study.

Methods:

We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005–2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network.

Results:

We found that 13% (95% confidence interval [CI], 7.6%–18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0–6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital.

Conclusions:

A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital’s infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.

The politics and ethics of hospital infection prevention and control: a qualitative case study of senior clinicians' perceptions of professional and cultural factors that influence doctors' attitudes and practices in a large Australian hospital

BACKGROUND

Hospital infection prevention and control (IPC) programs are designed to minimise rates of preventable healthcare-associated infection (HAI) and acquisition of multidrug resistant organisms, which are among the commonest adverse effects of hospitalisation. Failures of hospital IPC in recent years have led to nosocomial and community outbreaks of emerging infections, causing preventable deaths and social disruption. Therefore, effective IPC programs are essential, but can be difficult to sustain in busy clinical environments. Healthcare workers' adherence to routine IPC practices is often suboptimal, but there is evidence that doctors, as a group, are consistently less compliant than nurses. This is significant because doctors' behaviours disproportionately influence those of other staff and their peripatetic practice provides more opportunities for pathogen transmission. A better understanding of what drives doctors' IPC practices will contribute to development of new strategies to improve IPC, overall.

METHODS

This qualitative case study involved in-depth interviews with senior clinicians and clinician-managers/directors (16 doctors and 10 nurses) from a broad range of specialties, in a large Australian tertiary hospital, to explore their perceptions of professional and cultural factors that influence doctors' IPC practices, using thematic analysis of data.

RESULTS

Professional/clinical autonomy; leadership and role modelling; uncertainty about the importance of HAIs and doctors' responsibilities for preventing them; and lack of clarity about senior consultants' obligations emerged as major themes. Participants described marked variation in practices between individual doctors, influenced by, inter alia, doctors' own assessment of patients' infection risk and their beliefs about the efficacy of IPC policies. Participants believed that most doctors recognise the significance of HAIs and choose to [mostly] observe organisational IPC policies, but a minority show apparent contempt for accepted rules, disrespect for colleagues who adhere to, or are expected to enforce, them and indifference to patients whose care is compromised.

CONCLUSIONS

Failure of healthcare and professional organisations to address doctors' poor IPC practices and unprofessional behaviour, more generally, threatens patient safety and staff morale and undermines efforts to minimise the risks of dangerous nosocomial infection.

Disease persistence on temporal contact networks accounting for heterogeneous infectious periods

The infectious period of a transmissible disease is a key factor for disease spread and persistence. Epidemic models on networks typically assume an identical average infectious period for all individuals, thus allowing an analytical treatment. This simplifying assumption is, however, often unrealistic, as hosts may have different infectious periods, due, for instance, to individual host-pathogen interactions or inhomogeneous access to treatment. While previous work accounted for this heterogeneity in static networks, a full theoretical understanding of the interplay of varying infectious periods and time-evolving contacts is still missing. Here, we consider a susceptible-infectious-susceptible epidemic on a temporal network with host-specific average infectious periods, and develop an analytical framework to estimate the epidemic threshold, i.e. the critical transmissibility for disease spread in the host population. Integrating contact data for transmission with outbreak data and epidemiological estimates, we apply our framework to three real-world case studies exploring different epidemic contexts-the persistence of bovine tuberculosis in southern Italy, the spread of nosocomial infections in a hospital, and the diffusion of pandemic influenza in a school. We find that the homogeneous parametrization may cause important biases in the assessment of the epidemic risk of the host population. Our approach is also able to identify groups of hosts mostly responsible for disease diffusion who may be targeted for prevention and control, aiding public health interventions.

Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control

Background

Nosocomial, or healthcare-associated infections (HAI), exact a high medical and financial toll on patients, healthcare workers, caretakers, and the health system. Interpersonal contact patterns play a large role in infectious disease spread, but little is known about the relationship between health care workers’ (HCW) movements and contact patterns within a heath care facility and HAI. Quantitatively capturing these patterns will aid in understanding the dynamics of HAI and may lead to more targeted and effective control strategies in the hospital setting.

Methods

Staff at 3 urban university-based tertiary care hospitals in Canada completed a detailed questionnaire on demographics, interpersonal contacts, in-hospital movement, and infection prevention and control practices. Staff were divided into categories of administrative/support, nurses, physicians, and “Other HCWs” - a fourth distinct category, which excludes physicians and nurses. Using quantitative network modeling tools, we constructed the resulting HCW “co-location network” to illustrate contacts among different occupations and with locations in hospital settings.

Results

Among 3048 respondents (response rate 38%) an average of 3.79, 3.69 and 3.88 floors were visited by each HCW each week in the 3 hospitals, with a standard deviation of 2.63, 1.74 and 2.08, respectively. Physicians reported the highest rate of direct patient contacts (> 20 patients/day) but the lowest rate of contacts with other HCWs; nurses had the most extended (> 20 min) periods of direct patient contact. “Other HCWs” had the most direct daily contact with all other HCWs. Physicians also reported significantly more locations visited per week than nurses, other HCW, or administrators; nurses visited the fewest. Public spaces such as the cafeteria had the most staff visits per week, but the least mean hours spent per visit. Inpatient settings had significantly more HCW interactions per week than outpatient settings.

Conclusions

HCW contact patterns and spatial movement demonstrate significant heterogeneity by occupation. Control strategies that address this diversity among health care workers may be more effective than “one-strategy-fits-all” HAI prevention and control programs.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3093-x) contains supplementary material, which is available to authorized users.

Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics

Understanding transmission routes of hospital-acquired infections (HAI) is key to improve their control. In this context, describing and analyzing dynamic inter-individual contact patterns in hospitals is essential. In this study, we used wearable sensors to detect Close Proximity Interactions (CPIs) among patients and hospital staff in a 200-bed long-term care facility over 4 months. First, the dynamic CPI data was described in terms of contact frequency and duration per individual status or activity and per ward. Second, we investigated the individual factors associated with high contact frequency or duration using generalized linear mixed-effect models to account for inter-ward heterogeneity. Hospital porters and physicians had the highest daily number of distinct contacts, making them more likely to disseminate HAI among individuals. Conversely, contact duration was highest between patients, with potential implications in terms of HAI acquisition risk. Contact patterns differed among hospital wards, reflecting varying care patterns depending on reason for hospitalization, with more frequent contacts in neurologic wards and fewer, longer contacts in geriatric wards. This study is the first to report proximity-sensing data informing on inter-individual contacts in long-term care settings. Our results should help better understand HAI spread, parameterize future mathematical models, and propose efficient control strategies.

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

PURPOSE OF REVIEW

Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.

RECENT FINDINGS

Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).

SUMMARY

To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

Contact tracing with a real-time location system: A case study of increasing relative effectiveness in an emergency department

•

Contact tracing is an essential step in infectious disease control and prevention.

•

Using Electronic medical record (EMR) is challenging and misses a number of potential exposures.

•

Real time location system (RTLS) doubled the potential exposures list for pertussis disease beyond the conventional method of EMR-based contact identification

•

RTLS is more efficient and timely in the process of contact tracing.

•

Further studies with larger sample size are needed to confirm the findings.

Background

Contact tracing is the systematic method of identifying individuals potentially exposed to infectious diseases. Electronic medical record (EMR) use for contact tracing is time-consuming and may miss exposed individuals. Real-time location systems (RTLSs) may improve contact identification. Therefore, the relative effectiveness of these 2 contact tracing methodologies were evaluated.

Methods

During a pertussis outbreak in the United States, a retrospective case study was conducted between June 14 and August 31, 2016, to identify the contacts of confirmed pertussis cases, using EMR and RTLS data in the emergency department of a tertiary care medical center. Descriptive statistics and a paired t test (α = 0.05) were performed to compare contacts identified by EMR versus RTLS, as was correlation between pertussis patient length of stay and the number of potential contacts.

Results

Nine cases of pertussis presented to the emergency department during the identified time period. RTLS doubled the potential exposure list (P < .01). Length of stay had significant positive correlation with contacts identified by RTLS (ρ = 0.79; P = .01) but not with EMR (ρ = 0.43; P = .25).

Conclusions

RTLS doubled the potential pertussis exposures beyond EMR-based contact identification. Thus, RTLS may be a valuable addition to the practice of contact tracing and infectious disease monitoring.

Estimating the epidemic risk using non-uniformly sampled contact data

Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Electronic monitoring in combination with direct observation as a means to significantly improve hand hygiene compliance

Monitoring hand hygiene compliance among health care personnel (HCP) is an essential element of hand hygiene promotion programs. Observation by trained auditors is considered the gold standard method for establishing hand hygiene compliance rates. Advantages of observational surveys include the unique ability to establish compliance with all of the World Health Organization "My 5 Moments for Hand Hygiene" initiative Moments and to provide just-in-time coaching. Disadvantages include the resources required for observational surveys, insufficient sample sizes, and nonstandardized methods of conducting observations. Electronic and camera-based systems can monitor hand hygiene performance on all work shifts without a Hawthorne effect and provide significantly more data regarding hand hygiene performance. Disadvantages include the cost of installation, variable accuracy in estimating compliance rates, issues related to acceptance by HCP, insufficient data regarding their cost-effectiveness and influence on health care-related infection rates, and the ability of most systems to monitor only surrogates for Moments 1, 4, and 5. Increasing evidence suggests that monitoring only Moments 1, 4, and 5 provides reasonable estimates of compliance with all 5 Moments. With continued improvement of electronic monitoring systems, combining electronic monitoring with observational methods may provide the best information as part of a multimodal strategy to improve and sustain hand hygiene compliance rates among HCP.

The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

OBJECTIVE Antibiotic resistance is a major threat to public health. Resistance is largely driven by antibiotic usage, which in many cases is unnecessary and can be improved. The impact of decreasing overall antibiotic usage on resistance is unknown and difficult to assess using standard study designs. The objective of this study was to explore the potential impact of reducing antibiotic usage on the transmission of multidrug-resistant organisms (MDROs). DESIGN We used agent-based modeling to simulate interactions between patients and healthcare workers (HCWs) using model inputs informed by the literature. We modeled the effect of antibiotic usage as (1) a microbiome effect, for which antibiotic usage decreases competing bacteria and increases the MDRO transmission probability between patients and HCWs and (2) a mutation effect that designates a proportion of patients who receive antibiotics to subsequently develop a MDRO via genetic mutation. SETTING Intensive care unit INTERVENTIONS Absolute reduction in overall antibiotic usage by experimental values of 10% and 25% RESULTS Reducing antibiotic usage absolutely by 10% (from 75% to 65%) and 25% (from 75% to 50%) reduced acquisition rates of high-prevalence MDROs by 11.2% (P<.001) and 28.3% (P<.001), respectively. We observed similar effect sizes for low-prevalence MDROs. CONCLUSIONS In a critical care setting, where up to 50% of antibiotic courses may be inappropriate, even a moderate reduction in antibiotic usage can reduce MDRO transmission. Infect Control Hosp Epidemiol 2017;38:663-669.

Contact networks in the emergency department: Effects of time, environment, patient characteristics, and staff role

Emergency departments play a critical role in the public health system, particularly in times of pandemic. Infectious patients presenting to emergency departments bring a risk of cross-infection to other patients and staff through close proximity interactions or contacts. To understand factors associated with cross-infection risk, we measured close proximity interactions of emergency department staff and patients by radiofrequency identification in a working emergency department. The number of contacts (degree) is not related to patient demographic characteristics. However, the amount of time in close proximity (weighted degree) of patients with ED personnel did differ, with black patients having approximately 15 min more contact with staff than non-white patients. Patients arriving by EMS had fewer contacts with other patients than patients arriving by other means. There are differences in the number of contacts based on staff role and arrival mode. When crowding is low, providers have the most contact time with patients, while administrative staff have the least. However, when crowding is high, this differential is reversed. The effect of arrival mode is modified by the extent of crowding. When crowding is low, patients arriving by EMS had longer contact with administrative staff, compared to patients arriving by other means. However, when crowding is high, patients arriving by EMS had less contact with administrative staff compared to patients arriving by other means. Our findings should help designers of emergency care focus on higher risk situations for transmission of dangerous pathogens in an emergency department. For instance, the effects of arrival and crowding should be considered as targets for engineering or architectural interventions that could artificially increase social distances.

How bedside feedback improves head-of-bed angle compliance for intubated patients

One clinical defense against ventilator-associated pneumonia is maintaining the head-of-bed angle of ventilated patients above 30°. Most previous studies of head-of-bed angles using electronic monitoring have recorded compliance rates of less than 50%. The purpose of this study was to determine how bedside feedback of the head-of-bed angle affects bed angles set by healthcare workers. Electronic inclinometers were installed on 22 beds in an intensive care for a period of 38 days. Intubated patients were randomly assigned into two cohorts. One cohort received a graphical display of the bed angle adjacent to the in-room computer display. The head-of-bed angle of each intubated patient was continuously recorded, yielding 1,528 h of observation. The mean head-of-bed angle was 28.78° for beds with displays and 25.50° for those without, a significant difference. The most significant effects were for angles near 30°. Beds in the display cohort were three times as likely to be in a compliant position as beds in the no-display cohort. The results suggest that electronic bedside feedback improves head-of-bed angle compliance by raising angles slightly below the compliance threshold into compliance. This result may support studies of how compliant bed-angle protocols affect health outcomes.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes

BACKGROUND

Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement - paper diaries vs. wearable proximity sensors - that were applied concurrently to the same population, and we measured acceptability.

METHODS

We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability.

RESULTS

There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants' aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors.

CONCLUSION

Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants' attitudes

Background

Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement – paper diaries vs. wearable proximity sensors – that were applied concurrently to the same population, and we measured acceptability.

Methods

We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability.

Results

There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for <5 min contact duration (significantly lower than the following, p < 0.05), P = 86 % for 5-15 min, P = 89 % for 15-60 min, and P = 94 % for >60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants’ aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors.

Conclusion

Reporting and recording were not complete; reporting was particularly incomplete for contacts <5 min. The types of contact that both methods are capable of detecting are partly different. Participants appear to have overestimated the duration of their contacts. Conducting a study with diaries or wearable sensors was acceptable to and mostly easily done by participants. Both methods can be applied meaningfully if their specific limitations are considered and incompleteness is accounted for.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-1676-y) contains supplementary material, which is available to authorized users.

Hospital Clostridium difficile infection (CDI) incidence as a risk factor for hospital-associated CDI

BACKGROUND

Environmental risk factors for Clostridium difficile infections (CDIs) have been described at the room or unit level but not the hospital level. To understand the environmental risk factors for CDI, we investigated the association between institutional- and individual-level CDI.

METHODS

We performed a retrospective cohort study using the Healthcare Cost and Utilization Project state inpatient databases for California (2005-2011). For each patient's hospital stay, we calculated the hospital CDI incidence rate corresponding to the patient's quarter of discharge, while excluding each patient's own CDI status. Adjusting for patient and hospital characteristics, we ran a pooled logistic regression to determine individual CDI risk attributable to the hospital's CDI rate.

RESULTS

There were 10,329,988 patients (26,086 cases and 10,303,902 noncases) who were analyzed. We found that a percentage point increase in the CDI incidence rate a patient encountered increased the odds of CDI by a factor of 1.182.

CONCLUSIONS

As a point of comparison, a 1-percentage point increase in the CDI incidence rate that the patient encountered had roughly the same impact on their odds of acquiring CDI as a 55.8-day increase in their length of stay or a 60-year increase in age. Patients treated in hospitals with a higher CDI rate are more likely to acquire CDI.

How to Estimate Epidemic Risk from Incomplete Contact Diaries Data?

Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly available data describing the heterogeneity of the durations of human contacts.

Schools, offices, hospitals play an important role in the spreading of epidemics. Information about interactions between individuals in such contexts can help understand the patterns of transmission and design ad hoc immunization strategies. Data about contacts can be collected through various techniques such as diaries or proximity sensors. Here, we first ask if the corresponding datasets give similar predictions of the epidemic risk when they are used to build a network of contacts among individuals. Not surprisingly, the answer is negative: indeed, if we consider data from sensors as the ground truth, diaries are affected by low participation rate, underreporting and overestimation of durations. Is it however possible, despite these biases, to use data from contact diaries to obtain sensible epidemic risk predictions? We show here that, thanks to the structural similarities between the two networks, it is possible to use the contact diaries to build surrogate versions of the contact network obtained from the sensor data, such that both yield the same epidemic risk estimation. We show that the construction of such surrogate networks can be performed using solely the information contained in the contact diaries, complemented by publicly available data on the heterogeneity of cumulative contact durations between individuals.

Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated. We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures. Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.

Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks

Contacts between individuals play an important role in determining how infectious diseases spread. Various methods to gather data on such contacts co-exist, from surveys to wearable sensors. Comparisons of data obtained by different methods in the same context are however scarce, in particular with respect to their use in data-driven models of spreading processes. Here, we use a combined data set describing contacts registered by sensors and friendship relations in the same population to address this issue in a case study. We investigate if the use of the friendship network is equivalent to a sampling procedure performed on the sensor contact network with respect to the outcome of simulations of spreading processes: such an equivalence might indeed give hints on ways to compensate for the incompleteness of contact data deduced from surveys. We show that this is indeed the case for these data, for a specifically designed sampling procedure, in which respondents report their neighbors with a probability depending on their contact time. We study the impact of this specific sampling procedure on several data sets, discuss limitations of our approach and its possible applications in the use of data sets of various origins in data-driven simulations of epidemic processes.

Diabetes as an increasingly common comorbidity among patient hospitalizations for tuberculosis in the USA

OBJECTIVE

Diabetes is a risk factor for active tuberculosis (TB). The purpose of this paper was to estimate the risk of hospitalization for TB with and without a secondary diagnosis of diabetes in groups with different ethnic backgrounds.

RESEARCH DESIGN AND METHODS

We used the Nationwide Inpatient Sample from 1998 to 2011, identifying all patients with a primary diagnosis of TB and/or a secondary diagnosis of diabetes (type 1 or type 2) or HIV. Next, we performed logistic regression to investigate the association of diabetes status, HIV status, and race (and the interaction of diabetes and race) with the risk of hospitalization with a primary diagnosis of TB. We also included a time covariate, to determine whether potential risk factors changed during the study period.

RESULTS

Controlling for HIV status, diabetes did not increase the odds of TB in white and black patients. However, in Hispanic and Asian/Pacific Islander patients, diabetes increased the odds of TB by a factor of 1.7 (95% CI 1.51 to 1.83). Asian/Pacific Islanders who had diabetes but not HIV experienced 26.4 (95% CI 23.1 to 30.1) times the odds of TB relative to the white males without diabetes or HIV. In addition, the percent of TB cases that belong to these high-risk groups (Asian/Pacific Islander/Hispanic diabetics) has more than doubled from 4.6% in 1998 to 9.6% in 2011.

CONCLUSIONS

In specific demographic groups, diabetes was a strong risk factor for hospital admissions for TB.

Using Computer Vision and Depth Sensing to Measure Healthcare Worker-Patient Contacts and Personal Protective Equipment Adherence Within Hospital Rooms

This prospective study of cellulitis identified β-hemolytic streptococci as the dominating cause in all investigated subgroups. Group C/G streptococci were more frequently detected than group A streptococci. No single clinical feature substantially increased the probability of confirmed streptococcal etiology.

Background. We determined the feasibility of using computer vision and depth sensing to detect healthcare worker (HCW)-patient contacts to estimate both hand hygiene (HH) opportunities and personal protective equipment (PPE) adherence.

Methods. We used multiple Microsoft Kinects to track the 3-dimensional movement of HCWs and their hands within hospital rooms. We applied computer vision techniques to recognize and determine the position of fiducial markers attached to the patient's bed to determine the location of the HCW's hands with respect to the bed.

To measure our system's ability to detect HCW-patient contacts, we counted each time a HCW's hands entered a virtual rectangular box aligned with a patient bed. To measure PPE adherence, we identified the hands, torso, and face of each HCW on room entry, determined the color of each body area, and compared it with the color of gloves, gowns, and face masks. We independently examined a ground truth video recording and compared it with our system's results.

Results. Overall, for touch detection, the sensitivity was 99.7%, with a positive predictive value of 98.7%. For gowned entrances, sensitivity was 100.0% and specificity was 98.15%. For masked entrances, sensitivity was 100.0% and specificity was 98.75%; for gloved entrances, the sensitivity was 86.21% and specificity was 98.28%.

Conclusions. Using computer vision and depth sensing, we can estimate potential HH opportunities at the bedside and also estimate adherence to PPE. Our fine-grained estimates of how and how often HCWs interact directly with patients can inform a wide range of patient-safety research.

Missed Opportunities to Diagnose Tuberculosis Are Common Among Hospitalized Patients and Patients Seen in Emergency Departments

Background. Delayed diagnosis of tuberculosis (TB) may lead to worse outcomes and additional TB exposures.

Methods. To estimate the potential number of misdiagnosed TB cases, we linked all hospital and emergency department (ED) visits in California′s Healthcare Cost and Utilization Project (HCUP) databases (2005–2011). We defined a potential misdiagnosis as a visit with a new, primary diagnosis of TB preceded by a recent respiratory-related hospitalization or ED visit. Next, we calculated the prevalence of potential missed TB diagnoses for different time windows. We also computed odds ratios (OR) comparing the likelihood of a previous respiratory diagnosis in patients with and without a TB diagnosis, controlling for patient and hospital characteristics. Finally, we determined the correlation between a hospital′s TB volume and the prevalence of potential TB misdiagnoses.

Results. Within 30 days before an initial TB diagnosis, 15.9% of patients (25.7% for 90 days) had a respiratory-related hospitalization or ED visit. Also, within 30 days, prior respiratory-related visits were more common in patients with TB than other patients (OR = 3.83; P < .01), controlling for patient and hospital characteristics. Respiratory diagnosis-related visits were increasingly common until approximately 90 days before the TB diagnosis. Finally, potential misdiagnoses were more common in hospitals with fewer TB cases (ρ = −0.845; P < .01).

Conclusions. Missed opportunities to diagnose TB are common and correlate inversely with the number of TB cases diagnosed at a hospital. Thus, as TB becomes infrequent, delayed diagnoses may increase, initiating outbreaks in communities and hospitals.

Combining high-resolution contact data with virological data to investigate influenza transmission in a tertiary care hospital

OBJECTIVE

Contact patterns and microbiological data contribute to a detailed understanding of infectious disease transmission. We explored the automated collection of high-resolution contact data by wearable sensors combined with virological data to investigate influenza transmission among patients and healthcare workers in a geriatric unit.

DESIGN

Proof-of-concept observational study. Detailed information on contact patterns were collected by wearable sensors over 12 days. Systematic nasopharyngeal swabs were taken, analyzed for influenza A and B viruses by real-time polymerase chain reaction, and cultured for phylogenetic analysis.

SETTING

An acute-care geriatric unit in a tertiary care hospital.

PARTICIPANTS

Patients, nurses, and medical doctors.

RESULTS

A total of 18,765 contacts were recorded among 37 patients, 32 nurses, and 15 medical doctors. Most contacts occurred between nurses or between a nurse and a patient. Fifteen individuals had influenza A (H3N2). Among these, 11 study participants were positive at the beginning of the study or at admission, and 3 patients and 1 nurse acquired laboratory-confirmed influenza during the study. Infectious medical doctors and nurses were identified as potential sources of hospital-acquired influenza (HA-Flu) for patients, and infectious patients were identified as likely sources for nurses. Only 1 potential transmission between nurses was observed.

CONCLUSIONS

Combining high-resolution contact data and virological data allowed us to identify a potential transmission route in each possible case of HA-Flu. This promising method should be applied for longer periods in larger populations, with more complete use of phylogenetic analyses, for a better understanding of influenza transmission dynamics in a hospital setting.