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Understanding provider-level properties that influence the transmission of healthcare associated infections using network analysis
Journal Pre-proof Understanding provider-level properties that influence the transmission of healthcare associated infections using network analysis Hyojung Kang, Marika E. Waselewski, Jennifer M. Lobo
PII: DOI: Reference:
S2211-6923(18)30131-0 https://doi.org/10.1016/j.orhc.2019.100223 ORHC 100223
To appear in:
Operations Research for Health Care
Received date : 1 November 2018 Accepted date : 21 October 2019 Please cite this article as: H. Kang, M.E. Waselewski and J.M. Lobo, Understanding provider-level properties that influence the transmission of healthcare associated infections using network analysis, Operations Research for Health Care (2019), doi: https://doi.org/10.1016/j.orhc.2019.100223. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier Ltd.
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Understanding Provider-level Properties that Influence the Transmission of Healthcare Associated Infections Using Network Analysis
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Hyojung Kang, PhD;1 Marika E. Waselewski, MPH;2 Jennifer M. Lobo, PhD3 1
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA 2
Department of Medicine, Division of Infectious Diseases and International Health, School of Medicine, University of Virginia, Charlottesville, VA, USA 3
Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
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Corresponding Author: Jennifer M. Lobo, Ph.D. Department of Public Health Science Hospital West, 3rd Floor, Room 3003 PO Box 800717 Charlottesville, VA 22908-0717 [email protected] (O) 434-924-2813; (F) 434-924-8437
Keywords: hospital associated infections; electronic medical records; provider activity logs; Poisson regression; network model
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Word Count: 3341
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ABSTRACT The goal of this study is to determine which provider-level properties (e.g., provider types, patient contact
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factors) of healthcare workers (HCW) have the greatest impact on the transmission of healthcare associated infections (HAIs). This study focused on Carbapenem-resistant Enterobacteriaceae (CRE) acquisition for patients who stayed in a long-term acute care hospital (LTACH) in central Virginia during July and August 2014. We used both patient data (e.g., bed movement, screening results for CRE) and provider activity data documented through the electronic medical record. We created a network of patients for each HCW and performed Poisson regression analysis including the network measures. A total of 204 providers saw at least one of the nine positive patients who stayed in the LTACH over the
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study period. From the Poisson regression, provider types, total number of patients each provider saw, LTACH workdays, average number of patients per day during LTACH workdays, and the provider’s network were associated with the frequency of case contact. Our study demonstrated that in addition to
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patient data, provider activity logs that show provider-level properties can be used to assess the role of
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healthcare workers in transmitting HAIs and highlight risk mitigation opportunities.
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1. Introduction Healthcare-associated infections (HAIs) are any infection developed while receiving medical or surgical
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care [1]. HAIs have significant impact on patient health. In the U.S., approximately five to ten percent of hospitalized patients develop an HAI, and 99,000 people die from HAIs each year [2]. The economic burden of HAIs on health systems is staggering: overall annual direct HAI hospital costs are estimated to be $28-45 billion dollars [1]. HAIs caused by antibiotic resistant pathogens, like Clostridiodes difficile, Methicillin-resistant Pseudomonas aeruginosa (MRSA), and Vancomycin-resistant Enterococcus (VRE), are a growing concern in the U.S. It is estimated that more than two million people have antibioticresistant infections every year, resulting in at least 23,000 deaths [3]. In particular, Carbapenem-resistant
resistance to many common antibiotics [4].
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Enterobacteriaceae (CRE) have been recognized as having an immediate public health threat due to their
Prior research has shown that patients who are elderly, critically ill, have longer length of stay before
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infection, and stay in intensive care units (ICUs) or long-term acute care hospitals (LTACHs) are more likely to be infected with CRE [5–8]. Other risk factors include organ transplantation, invasive procedures (e.g., mechanical ventilation, blood catheters), and some medications (cephalosporins, carbapenems) [5,6,9]. ICU-wide factors such as the prevalence of colonized patients and the rate of antibiotic use in
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patients have also been documented [10]. Transmission can occur through direct or indirect contact with infected people or contaminated surfaces. Invasive medical devices not adequately sterilized are common sources of infection [5,6,11–13]. CRE can also be transmitted through contaminated environmental sites. Many studies have identified the bed area, sinks, taps, and drains are common environmental reservoirs for bacteria [14–19].
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Healthcare workers (HCWs) are also potential vectors of transmission for infections. Contact with stool or wounds of patients with CRE may increase the risk of transmission to patients, in particular when they fail to properly scrub their hands or wear personal protective equipment [12]. Studies have found that the hands of HCWs, along with other sources, can be colonized and can provide routes of transmission for
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CRE species in ICUs [20–23]. Other studies have reported the presence of CRE species and/or other bacteria such as MRSA on items HCWs carry or wear, including stethoscopes, identification badges,
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gowns, and gloves, through which the bacteria can be transmitted to patients [24–26]. In order to understand the epidemiology of CRE in a hospital setting, observational studies have been conducted, and they investigated CRE genotypes, patient characteristics, and identified patient risk factors for the CRE pathogen [5,20,21,24,27,28]. One study investigated colonization of four pathogenic bacteria isolated from badges and lanyards worn by HCWs and identified that there were no statistical differences between doctors and nurses in total bacteria count, though doctors were over four times more likely to carry methicillin-susceptible Staphylococcus aureus (MSSA) on lanyards compared with nurses [25].
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However, this study did not include CRE as one of the examined species. Also, the study did not incorporate patients into the analysis, which limits its inferences about the role of HCWs in infection transmission between patients. Retrospective case-control studies have been used to determine risk factors
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associated with CRE infections, compare outcomes for CRE infections, and compare mortality with and without antibiotic resistance [6,9–11,28,29]. Sources of acquisition and transmission identified in these studies can help design intervention policies. One case-control study has focused on HCWs as a potential
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vector of CRE infection transmission [30].
The goal of this study is to determine which provider-level properties (e.g., provider types, patient contact factors) have the greatest impact on the transmission of HAIs using patient data and provider activity logs. While many studies have focused on the characteristic of patients that lead them to acquire HAI during their stay, the role of care providers in transmission of HAI has been relatively less studied. Our study shows that analyzing patient and provider interactions data using a social network approach can
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provide a new way to provide a better understanding of complex provider activities potentially associated with transmission of HAI. Utilization of such methods by healthcare institutions could allow for valuable insight into the development of interventions to limit the transmission of HAIs.
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2. Material and Methods 2.1. Data
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Study data was extracted from electronic medical records (EMR) from a central Virginia hospital. These records included datasets of patients who stayed in the LTACH and the HCWs who provided their care. We included all LTACH patients and patients in other units who shared HCWs with the LTACH patients, limited to days on which the HCW saw a patient in the LTACH. Data was limited to July and August 2014 due to consistency of data availability and presence of CRE positive patients in the unit; this type of analysis would not work if no CRE patients were admitted to the unit. Patients were required to have a
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minimum of one day length of stay in the LTACH. If a patient stayed in the LTACH for only one day and their HCW(s) saw other patients during the same day, then the patient’s data was included. For patients that had part of their stay outside of the two-month study period, we only included their data from the dates during the study period. The LTACH was chosen because of the regularity of CRE screening
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allowing for the most accurate CRE diagnosis dates. Figure 1 provides an overview of the criteria for patient populations used in this study.
Patient data included bed movement data, to understand when and where patients stayed in the LTACH,
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and screening results for CRE. Patients were screened within 48 hours of admission to the LTACH and weekly thereafter during the study period. Provider data included interactions with patients, documented through flowsheet entries, procedures, and the medication administration records. Combining these datasets, our data included over 1200 unique EMR log descriptions. An infectious disease physician and the authors reviewed the log descriptions to decide which ones would likely constitute provider-patient contact. Examples of included logs were collecting vital signs, administering medication, and performing
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imaging studies; examples of excluded logs were note entry in the EMR, notification of wasted medication during preparation, and telephone encounters. HCWs utilized were required to have physical contact with patients and included nurses, therapists, and patient care assistants. The complete set of
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HCWs was culled from a list of over 200 roles of staff members that interacted with patient charts. We excluded front desk, quality assurance, coder employees, and other staff members that likely had no direct
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patient contact. Physicians were excluded as timing and content of encounters could not be reliably associated with charted interactions. For example, at this institution physicians have up to two weeks to document notes in the chart. The delay in documentation may not provide accurate information about the timing of their interactions with patients. Included HCWs were then grouped by staff role types: case managers, patient care assistants, therapists, specialty technicians, and other. Additionally, the HCWs were limited to providers who saw more than one patient on at least one day. Database development and analysis was approved by the University Institutional Review Board.
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2.2 Poisson Regression Based on Social Network Measures
We assumed that HCWs who see more positive patients may be more likely to transmit infection between patients. To understand factors associated with provider contact frequency with positive CRE patients we
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built a Poisson regression model that includes provider-related characteristics and measures drawn from each provider’s patient network. The parameters were estimated using maximum likelihood estimation: log(Num_cases)= 0 + 1 RoleType + 2 Num_pts + 3 Avg_PPD + 4 Work_days + 5 Avg_deg
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+ 6 Avg_cls + 7 Net_den +
(1)
The number of unique positive patients a HCW contacted (Num_cases) was formulated as a function of provider characteristics and patterns of interactions with patients. Provider characteristics included provider role (RoleType), the total number of unique patients they saw during the two months including non-LTACH workdays (Num_pts), average unique patients per day (Avg_PPD) – for each provider this
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was the sum of the unique patients per day averaged over work days, and number of LTACH workdays (Work_days) during the study period. While these parameters show an aspect of HCWs’ contact with patients, they do not represent how patients who shared the same HCW are associated. To address this relationship between patients that can be a basis of HAI transmission through HCWs, we adopted a social
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network analysis approach. A network was developed for each HCW over the study period: each patient seen by the HCW represents a node, and patients are connected if they shared the HCW on the same day.
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Network measures, including the average degree centrality (Avg_deg), average clustering coefficient (Avg_cls), and network density (Net_den), were used as predictors in the Poisson regression model. Degree centrality represents the number of links each node has within a network. A clustering coefficient ranging between 0 and 1 represents the degree to which patients tend to cluster together. Network density represents the proportion of the actual connections to the potential connections in a network. The social network and Poisson regression model were built and analyzed in R.
3.1 Model Parameter Estimation
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3. Results
Patients with shared HCW interactions were defined as patients treated by the same HCW on the same
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day. The examples of treatment included giving/stopping new medication, taking vitals, and performing imaging tests. HCWs who only saw one patient per day were excluded from all analyses because they never caused shared interactions.
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A total of 542 unique HCWs saw patients who stayed in the LTACH over the study period. Of them, 43 providers saw only one patient during the period and were therefore excluded from the analysis. The remaining 499 providers saw a total of 3730 distinct patients in the LTACH and other units during the study period with a median of 59 (IQR: 37, 110) patients each. These HCWs worked a median of 1 day in the LTACH during the two months (IQR: 1, 5; max of 51 days). On days providers worked in the LTACH they saw 1309 distinct patients, in either the LTACH or another inpatient unit, with a median of
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5 average patients per day (IQR: 3, 8). Figure 2 shows the number of positive patients each HCW encountered during the same period. A total of 191 out of 499 HCWs (38.3%) saw one or more cases in the LTACH with a median of 2 positives (IQR: 1, 6). Six HCWs saw all nine cases during the period.
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The degree to which positive patients affect susceptible patients may be influenced by their contact frequency with HCWs and the HCWs’ contact frequency with other patients. The average number of
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HCWs per positive patient per day was 9.21 (minimum 6.53, maximum 11.36). The average number of patients these HCWs saw per day (averaged per positive patient then across all positive patients) was 5.38 (minimum 4.90, maximum 7.11). 3.2. Network Analysis
A social network model of patients who shared providers was built for each of the 499 HCWs. Note that a network was built based on patients who had a shared provider only during the LTACH work days. In
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other words, patients who were seen by the same provider during the days the provider did not cover LTACH was not included in the network model because the focus of this shared network was on LTACH. Figure 3 provides example networks for four HCW roles: case manager, therapist, nurse, and patient care assistant. Each node in the network represents a patient who was seen by the provider. The large dots
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represent positive patients. If patients were seen by the same provider during the same day, the nodes are connected by edges. The thickness of the edges indicates the frequency of the connections between the nodes. These examples show that networks of providers vary by provider type in terms of size and degree
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of connections, clustering, and density.
Table 1 summarizes the average network measures by provider role type. Overall, the case manager group had the highest average clustering coefficient and density while the average degree centrality of the second highest. In other words, LTACH patients seen by case managers tended to be connected to more patients and clustered together. The networks of nurses had the low average degree centrality and clustering coefficient. The therapist group had the highest average degree centrality while their average
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network density was the lowest among the groups. Note that individual provider has their own network and have different network measures, and we used the individual values in the Poisson regression. In other words, the network measures were not fixed by the provider role.
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3.3 Poisson Regression Poisson regression is commonly used for modeling counting variables, but it assumes equidispersion (the
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conditional mean and variance are the same). In our data, the mean and variance of the number of unique positive patients a HCW contacted including 0 case contact (Num_cases) was 1.43 and 5.76, respectively. For over-dispersed count outcome variables, negative binomial regression should be used. We tested a hypothesis of equidispersion in the Poisson regression (1) against the alternative of overdispersion using a function dispersiontest in R [31]. The result indicated there is no enough evidence to reject the null hypothesis (p-value: 0.99). This is probably because of excessive 0s in our data. We also compared the log-likelihoods of a negative binomial regression model and a Poisson regression model using a function
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odTest in R, and the result showed that a Poisson regression model performs better than a negative regression model [32]. Therefore, we choice to use the Poisson regression model. The overall fit of the Poisson regression model was tested using the residual deviance. The deviance chi-square goodness-of-fit
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test showed that the residual difference is small enough under the Poisson assumption, indicating that the model fits the data (p-value = 0.99).
Table 2 summarizes the results of the Poisson Regression model. The coefficients (β) indicate the change
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in the expected log count for a one-unit increase in continuous predictor variables and the expected log count between the test and reference group for categorical variables. The difference of two logs (log (xi) – log (xj)) is equal to the log of their ratio (log (xi /xj)). Therefore, we can interpret the model in terms of incidence-risk ratio (IRR) by taking exponentials of the coefficients (β). In addition, for the parameter estimates robust standard errors were adopted to adjust for mild violation of the assumption of Poisson distribution explained above.
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The results showed that the patient care assistant and therapist groups were the only HCW types that had a significant impact on the expected number of contacts with positive patients compared to the nurse group (=0.05). The patient care assistant and therapist groups are likely to contact positive patients 1.18
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and 1.28 times more than the reference group, holding other variables constant, respectively. However, the test for the overall effect of HCW role indicated that Prov_role is a statistically significant predictor
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of Num_cases (p-value < 0.05). The total number of patients (Num_pts) and average number of patients per day (Avg_PPD) were significant and had a negative association with the number of cases HCWs saw. If the total number of patients a HCW took care of during the two months increases by one patient, the expected contacts with positive patients decreased by a 0.7%. Similarly, for an increase in the average number of patients HCWs saw per LTACH workday, the expected number of contacts with positive cases decreases by 6.4%. On the other hand, for each additional LTACH workday a HCW it is 1.019 times more likely to see a positive
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patient.
The results also showed significant associations with HCW network measures. The expected number of positive contacts increased with increasing average degree centrality, when controlling for the number of
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patients and working days (IRR = 1.104). The average degree of centrality is increased when patients tend to have more connections within the network. The Poisson regression result indicates that a HCW has a greater chance of contacting positive patients if their patients are connected with more patients each day.
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Holding all other variables constant, a one percent increase in the average clustering coefficient increases the estimated positive contact by a factor of 1.658, but this was not statistically significant. The model also indicated that as the ratio of the number of actual connections between shared provider patients to the total possible connections increases by one unit the expected positive contacts decreases at the rate of 0.037. A network density of 100% indicates a perfectly dense network where all nodes are connected and a group of patients is continuously seen by the HCW over time. It is likely impractical to maintain this
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network long term. A HCW with a high network density might work fewer days in the LTACH, thus reducing the chance of contacting with CRE positives in the unit. 4. Discussion
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As the EMR system has been widely adopted, a large amount of patient and provider data is collected and stored in clinical data repositories. This big data has facilitated studies that predict patient risk and
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outcomes as well as system performances [33,34]. However, patient-provider relationships have primarily been studied through detailed observation with a small sample size. While these studies add more granularity to interactions, they are not feasible at a large scale considering task complexity of patients and providers [35]. Our study demonstrated how combined data from different sources can be utilized to assess the role of HCWs in transmitting HAIs between patients on a large scale.
We analyzed the data using Poisson regression. We anticipated HCW characteristics and patterns of interactions with patients would be associated with positive contact frequency. For example, certain
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provider roles are more likely to see positive patients (higher likelihood of transmitting infection than others). Among the six groups of HCWs who saw patients in the LTACH, therapists had the highest probability of interacting with cases, followed by patient care assistants. In fact, case managers had a
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higher chance of contacting cases than therapists, but its impact was not statistically significant. Also, case managers had an IRR greater than 1 with nurses being the comparator, but it was not significant. The small number of case managers and variability in the number of patients and working days for case managers may be reasons the result was not significant. Interestingly, HCWs who saw fewer patients per
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day during the LTACH workdays were more likely to encounter CRE positive patients. This is potentially due to the fact that HCWs involved in the care of CRE patients are often not able to take care of as many other patients due to the amount of care required by these sicker patients. If it is not possible to restrict the number of patients a HCW sees when one of their patients is CRE positive through cohorting, there could be more targeted interventions for personal protective equipment, hand hygiene, and sterilization of items
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HCWs wear, including ID badges. Also, given that frequency of contact with positives may be correlated with transmission to non-colonized patients, understanding the likelihood of contact with positive patients could influence staffing decisions and HCWs in need of targeted interventions to reduce colonization.
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Throughout this project there were many data challenges and lessons learned. Accessing provider data through EMR logs and culling the data to include only direct patient interaction was a painstaking process
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that required the aid of EMR information technology programmers and an infectious disease clinician. To allow for more accurate predictions of transmission dates, we chose a study period during which regular CRE perirectal screening was performed in high-risk units. Vetting data from the different sources, including patient medical records and colonization dates, was completed to ensure data integrity. While this process took time, more accurate results are possible using properly vetted data. A more systematic approach using decision rules may help determine relevant activities more efficiently and accurately. There were several limitations in this study. First, due to the limited time frame and hospital units where
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regular CRE screening occurred, our study had a small number of cases. The study was able to demonstrate how the modeling techniques combined with patient and provider datasets can be applied to understand HCW influence on infection transmission. The application of the techniques in larger data
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settings may enable a more accurate estimation of the degree of the transmission through HCWs. Second, clinicians were not able to be included as their interactions with patients were not reliably determined from the retrospective EMR data. While the exclusion of clinicians remains a limitation, we
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used provider data that included reliable information about tasks performed by HCWs who may have closer interactions with patients. Other technologies such as wearable devices tracking movement may help in the future to delineate interaction intensity and timing. Third, we excluded HCWs who saw only 1 patient per day. It may be possible they used the same clothes and badge through which infection could be still transmitted between two different days. However, the strength of connection between patients who shared HCWs on the same day and a next day would be
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different. Also, we think it is difficult to assume any behaviors of HCWs without directly interviewing or observing them. Therefore, we took a conservative approach in estimating transmission possibilities and only used same-day interactions.
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Fourth, patients admitted prior to the beginning of the two month period were included in the study, though we only included their data from dates after the start of the study. These patients would have had
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previous contact with HCWs where the infection may have taken place and that data was not recorded. Lastly, this study used day-level interactions that do not account for frequency of interactions throughout a day. In the provider data, it was not always clear if the time stamps of tasks involving patient interactions could be trusted. For patient safety and quality, it is recommended that HCWs document as close as possible to the actual event of care [36]. However, various factors including the complexity of a patient’s health problems and documenting locations (bedside vs. central station desks) affect timing and frequency of charting [37,38]. When provider data that includes more accurate time stamps of interactions
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is available, we will be able to consider the impact of frequency and interaction order on transmission risk.
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5. Conclusion
In summary, our study demonstrated the benefits of leveraging both patient and HCW interactions data from an ongoing CRE outbreak to understand the potential role of HCWs in HAI transmission. The model results indicated that working characteristics and patient network structures of HCWs are associated with
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the frequency of contacts with positive patients. If HCWs having a higher rate of contacts with positive patients are more likely to transmit infection between patients, understanding those provider-related features can help better examine the extent and pathways HCWs transmit HAI between patients. Furthermore, this understanding could allow healthcare institutions for valuable insight into the development of interventions to limit the transmission of HAIs through HCWs.
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6. Acknowledgements and Funding
This research was supported by the Coulter Translational Research Partnership and the University of Virginia Health Center.
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Tables: Table 1. Summary of measures for networks of 499 HCWs. Average degree
Average clustering
Average network
centrality
coefficient
density
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Provider type Number of HCWs
279
5.15
0.79
0.72
Case Manager
20
8.92
0.99
0.77
Patient Care Assistant
92
8.70
0.92
0.76
Therapist
74
8.94
0.88
0.58
Specialty Technician
20
8.87
0.79
0.67
Other
14
0.79
0.69
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Nurses
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6.75
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Table 2. Poisson Regression Model Results.
Incident rate ratio Robust standard Coefficient (β)
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[95% confidence error
p-value
interval]
Intercept
1.193
0.211
3.296 [2.178,4.987]
< 0.001
0.591
0.41
1.806 [0.809,4.033]
0.149
0.166
0.082
1.18 [1.005,1.386]
0.043
0.243
0.083
1.275 [1.084,1.5]
0.003
0.244
0.836 [0.519,1.348]
0.463
0.178
0.795 [0.561,1.126]
0.197
RoleType (Nurses) Case Manager Patient Care
Therapist Specialty
-0.179 Technician
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Assistant
-0.229
Num_pts
-0.007
0.002
0.993 [0.99,0.996]
< 0.001
Avg_PPD
-0.066
0.03
0.936 [0.883,0.993]
0.027
Work_days
0.019
0.003
1.019 [1.012,1.026]
< 0.001
0.099
0.011
1.104 [1.081,1.127]
< 0.001
0.506
0.273
1.658 [0.971,2.833]
0.064
-3.29
0.245
0.037 [0.023,0.060]
< 0.001
Avg_cls Net_den
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Avg_deg
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Other
Num_pts: total number of patients each provider saw during the two months; Avg_PPD: average patients each provider saw per day during LTACH workdays; Work_days: LTACH workdays;
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Avg_deg: average degree centrality; Avg_cls: average cluster coefficient; Net_den: network density
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Figure Captions: Figure 1. Diagram of the patient populations used in the analyses. (LTACH: long-term acute care hospital;
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HCWs: Healthcare Workers; np: number of patients; nh: number of HCWs; *: days HCWs worked in the LTACH included in the analysis)
Figure 2. Distribution of number of positive patients each HCW encountered during the period.
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Figure 3. Example HCW networks for (a) case manager, (b) therapist, (c) nurse, and (d) patient care assistant.
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2. All patients who stayed in the LTACH in July or August 2014 (np = 64)
3. HCWs who saw LTACH patients in July or August 2014 (nh = 542)
No
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Removed (nh = 43)
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1. All patients who stayed in the inpatient units or LTACH in July or August 2014
4. Patients who were seen by these HCWs in July or August 2014 (np = 3730)
5. Saw more than one patient per day during LTACH workdays?
Yes
6. HCWs included in the study* (nh = 499)
: patients : HCWs
7. Patients included for LTACH shared provider analysis (np = 1309)
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PII: DOI: Reference:
S2211-6923(18)30131-0 https://doi.org/10.1016/j.orhc.2019.100223 ORHC 100223
To appear in:
Operations Research for Health Care
Received date : 1 November 2018 Accepted date : 21 October 2019 Please cite this article as: H. Kang, M.E. Waselewski and J.M. Lobo, Understanding provider-level properties that influence the transmission of healthcare associated infections using network analysis, Operations Research for Health Care (2019), doi: https://doi.org/10.1016/j.orhc.2019.100223. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier Ltd.
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Understanding Provider-level Properties that Influence the Transmission of Healthcare Associated Infections Using Network Analysis
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Hyojung Kang, PhD;1 Marika E. Waselewski, MPH;2 Jennifer M. Lobo, PhD3 1
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA 2
Department of Medicine, Division of Infectious Diseases and International Health, School of Medicine, University of Virginia, Charlottesville, VA, USA 3
Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA
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Corresponding Author: Jennifer M. Lobo, Ph.D. Department of Public Health Science Hospital West, 3rd Floor, Room 3003 PO Box 800717 Charlottesville, VA 22908-0717 [email protected] (O) 434-924-2813; (F) 434-924-8437
Keywords: hospital associated infections; electronic medical records; provider activity logs; Poisson regression; network model
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Word Count: 3341
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ABSTRACT The goal of this study is to determine which provider-level properties (e.g., provider types, patient contact
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factors) of healthcare workers (HCW) have the greatest impact on the transmission of healthcare associated infections (HAIs). This study focused on Carbapenem-resistant Enterobacteriaceae (CRE) acquisition for patients who stayed in a long-term acute care hospital (LTACH) in central Virginia during July and August 2014. We used both patient data (e.g., bed movement, screening results for CRE) and provider activity data documented through the electronic medical record. We created a network of patients for each HCW and performed Poisson regression analysis including the network measures. A total of 204 providers saw at least one of the nine positive patients who stayed in the LTACH over the
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study period. From the Poisson regression, provider types, total number of patients each provider saw, LTACH workdays, average number of patients per day during LTACH workdays, and the provider’s network were associated with the frequency of case contact. Our study demonstrated that in addition to
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patient data, provider activity logs that show provider-level properties can be used to assess the role of
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healthcare workers in transmitting HAIs and highlight risk mitigation opportunities.
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1. Introduction Healthcare-associated infections (HAIs) are any infection developed while receiving medical or surgical
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care [1]. HAIs have significant impact on patient health. In the U.S., approximately five to ten percent of hospitalized patients develop an HAI, and 99,000 people die from HAIs each year [2]. The economic burden of HAIs on health systems is staggering: overall annual direct HAI hospital costs are estimated to be $28-45 billion dollars [1]. HAIs caused by antibiotic resistant pathogens, like Clostridiodes difficile, Methicillin-resistant Pseudomonas aeruginosa (MRSA), and Vancomycin-resistant Enterococcus (VRE), are a growing concern in the U.S. It is estimated that more than two million people have antibioticresistant infections every year, resulting in at least 23,000 deaths [3]. In particular, Carbapenem-resistant
resistance to many common antibiotics [4].
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Enterobacteriaceae (CRE) have been recognized as having an immediate public health threat due to their
Prior research has shown that patients who are elderly, critically ill, have longer length of stay before
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infection, and stay in intensive care units (ICUs) or long-term acute care hospitals (LTACHs) are more likely to be infected with CRE [5–8]. Other risk factors include organ transplantation, invasive procedures (e.g., mechanical ventilation, blood catheters), and some medications (cephalosporins, carbapenems) [5,6,9]. ICU-wide factors such as the prevalence of colonized patients and the rate of antibiotic use in
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patients have also been documented [10]. Transmission can occur through direct or indirect contact with infected people or contaminated surfaces. Invasive medical devices not adequately sterilized are common sources of infection [5,6,11–13]. CRE can also be transmitted through contaminated environmental sites. Many studies have identified the bed area, sinks, taps, and drains are common environmental reservoirs for bacteria [14–19].
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Healthcare workers (HCWs) are also potential vectors of transmission for infections. Contact with stool or wounds of patients with CRE may increase the risk of transmission to patients, in particular when they fail to properly scrub their hands or wear personal protective equipment [12]. Studies have found that the hands of HCWs, along with other sources, can be colonized and can provide routes of transmission for
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CRE species in ICUs [20–23]. Other studies have reported the presence of CRE species and/or other bacteria such as MRSA on items HCWs carry or wear, including stethoscopes, identification badges,
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gowns, and gloves, through which the bacteria can be transmitted to patients [24–26]. In order to understand the epidemiology of CRE in a hospital setting, observational studies have been conducted, and they investigated CRE genotypes, patient characteristics, and identified patient risk factors for the CRE pathogen [5,20,21,24,27,28]. One study investigated colonization of four pathogenic bacteria isolated from badges and lanyards worn by HCWs and identified that there were no statistical differences between doctors and nurses in total bacteria count, though doctors were over four times more likely to carry methicillin-susceptible Staphylococcus aureus (MSSA) on lanyards compared with nurses [25].
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However, this study did not include CRE as one of the examined species. Also, the study did not incorporate patients into the analysis, which limits its inferences about the role of HCWs in infection transmission between patients. Retrospective case-control studies have been used to determine risk factors
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associated with CRE infections, compare outcomes for CRE infections, and compare mortality with and without antibiotic resistance [6,9–11,28,29]. Sources of acquisition and transmission identified in these studies can help design intervention policies. One case-control study has focused on HCWs as a potential
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vector of CRE infection transmission [30].
The goal of this study is to determine which provider-level properties (e.g., provider types, patient contact factors) have the greatest impact on the transmission of HAIs using patient data and provider activity logs. While many studies have focused on the characteristic of patients that lead them to acquire HAI during their stay, the role of care providers in transmission of HAI has been relatively less studied. Our study shows that analyzing patient and provider interactions data using a social network approach can
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provide a new way to provide a better understanding of complex provider activities potentially associated with transmission of HAI. Utilization of such methods by healthcare institutions could allow for valuable insight into the development of interventions to limit the transmission of HAIs.
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2. Material and Methods 2.1. Data
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Study data was extracted from electronic medical records (EMR) from a central Virginia hospital. These records included datasets of patients who stayed in the LTACH and the HCWs who provided their care. We included all LTACH patients and patients in other units who shared HCWs with the LTACH patients, limited to days on which the HCW saw a patient in the LTACH. Data was limited to July and August 2014 due to consistency of data availability and presence of CRE positive patients in the unit; this type of analysis would not work if no CRE patients were admitted to the unit. Patients were required to have a
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minimum of one day length of stay in the LTACH. If a patient stayed in the LTACH for only one day and their HCW(s) saw other patients during the same day, then the patient’s data was included. For patients that had part of their stay outside of the two-month study period, we only included their data from the dates during the study period. The LTACH was chosen because of the regularity of CRE screening
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allowing for the most accurate CRE diagnosis dates. Figure 1 provides an overview of the criteria for patient populations used in this study.
Patient data included bed movement data, to understand when and where patients stayed in the LTACH,
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and screening results for CRE. Patients were screened within 48 hours of admission to the LTACH and weekly thereafter during the study period. Provider data included interactions with patients, documented through flowsheet entries, procedures, and the medication administration records. Combining these datasets, our data included over 1200 unique EMR log descriptions. An infectious disease physician and the authors reviewed the log descriptions to decide which ones would likely constitute provider-patient contact. Examples of included logs were collecting vital signs, administering medication, and performing
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imaging studies; examples of excluded logs were note entry in the EMR, notification of wasted medication during preparation, and telephone encounters. HCWs utilized were required to have physical contact with patients and included nurses, therapists, and patient care assistants. The complete set of
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HCWs was culled from a list of over 200 roles of staff members that interacted with patient charts. We excluded front desk, quality assurance, coder employees, and other staff members that likely had no direct
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patient contact. Physicians were excluded as timing and content of encounters could not be reliably associated with charted interactions. For example, at this institution physicians have up to two weeks to document notes in the chart. The delay in documentation may not provide accurate information about the timing of their interactions with patients. Included HCWs were then grouped by staff role types: case managers, patient care assistants, therapists, specialty technicians, and other. Additionally, the HCWs were limited to providers who saw more than one patient on at least one day. Database development and analysis was approved by the University Institutional Review Board.
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2.2 Poisson Regression Based on Social Network Measures
We assumed that HCWs who see more positive patients may be more likely to transmit infection between patients. To understand factors associated with provider contact frequency with positive CRE patients we
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built a Poisson regression model that includes provider-related characteristics and measures drawn from each provider’s patient network. The parameters were estimated using maximum likelihood estimation: log(Num_cases)= 0 + 1 RoleType + 2 Num_pts + 3 Avg_PPD + 4 Work_days + 5 Avg_deg
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+ 6 Avg_cls + 7 Net_den +
(1)
The number of unique positive patients a HCW contacted (Num_cases) was formulated as a function of provider characteristics and patterns of interactions with patients. Provider characteristics included provider role (RoleType), the total number of unique patients they saw during the two months including non-LTACH workdays (Num_pts), average unique patients per day (Avg_PPD) – for each provider this
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was the sum of the unique patients per day averaged over work days, and number of LTACH workdays (Work_days) during the study period. While these parameters show an aspect of HCWs’ contact with patients, they do not represent how patients who shared the same HCW are associated. To address this relationship between patients that can be a basis of HAI transmission through HCWs, we adopted a social
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network analysis approach. A network was developed for each HCW over the study period: each patient seen by the HCW represents a node, and patients are connected if they shared the HCW on the same day.
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Network measures, including the average degree centrality (Avg_deg), average clustering coefficient (Avg_cls), and network density (Net_den), were used as predictors in the Poisson regression model. Degree centrality represents the number of links each node has within a network. A clustering coefficient ranging between 0 and 1 represents the degree to which patients tend to cluster together. Network density represents the proportion of the actual connections to the potential connections in a network. The social network and Poisson regression model were built and analyzed in R.
3.1 Model Parameter Estimation
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3. Results
Patients with shared HCW interactions were defined as patients treated by the same HCW on the same
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day. The examples of treatment included giving/stopping new medication, taking vitals, and performing imaging tests. HCWs who only saw one patient per day were excluded from all analyses because they never caused shared interactions.
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A total of 542 unique HCWs saw patients who stayed in the LTACH over the study period. Of them, 43 providers saw only one patient during the period and were therefore excluded from the analysis. The remaining 499 providers saw a total of 3730 distinct patients in the LTACH and other units during the study period with a median of 59 (IQR: 37, 110) patients each. These HCWs worked a median of 1 day in the LTACH during the two months (IQR: 1, 5; max of 51 days). On days providers worked in the LTACH they saw 1309 distinct patients, in either the LTACH or another inpatient unit, with a median of
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5 average patients per day (IQR: 3, 8). Figure 2 shows the number of positive patients each HCW encountered during the same period. A total of 191 out of 499 HCWs (38.3%) saw one or more cases in the LTACH with a median of 2 positives (IQR: 1, 6). Six HCWs saw all nine cases during the period.
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The degree to which positive patients affect susceptible patients may be influenced by their contact frequency with HCWs and the HCWs’ contact frequency with other patients. The average number of
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HCWs per positive patient per day was 9.21 (minimum 6.53, maximum 11.36). The average number of patients these HCWs saw per day (averaged per positive patient then across all positive patients) was 5.38 (minimum 4.90, maximum 7.11). 3.2. Network Analysis
A social network model of patients who shared providers was built for each of the 499 HCWs. Note that a network was built based on patients who had a shared provider only during the LTACH work days. In
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other words, patients who were seen by the same provider during the days the provider did not cover LTACH was not included in the network model because the focus of this shared network was on LTACH. Figure 3 provides example networks for four HCW roles: case manager, therapist, nurse, and patient care assistant. Each node in the network represents a patient who was seen by the provider. The large dots
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represent positive patients. If patients were seen by the same provider during the same day, the nodes are connected by edges. The thickness of the edges indicates the frequency of the connections between the nodes. These examples show that networks of providers vary by provider type in terms of size and degree
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of connections, clustering, and density.
Table 1 summarizes the average network measures by provider role type. Overall, the case manager group had the highest average clustering coefficient and density while the average degree centrality of the second highest. In other words, LTACH patients seen by case managers tended to be connected to more patients and clustered together. The networks of nurses had the low average degree centrality and clustering coefficient. The therapist group had the highest average degree centrality while their average
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network density was the lowest among the groups. Note that individual provider has their own network and have different network measures, and we used the individual values in the Poisson regression. In other words, the network measures were not fixed by the provider role.
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3.3 Poisson Regression Poisson regression is commonly used for modeling counting variables, but it assumes equidispersion (the
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conditional mean and variance are the same). In our data, the mean and variance of the number of unique positive patients a HCW contacted including 0 case contact (Num_cases) was 1.43 and 5.76, respectively. For over-dispersed count outcome variables, negative binomial regression should be used. We tested a hypothesis of equidispersion in the Poisson regression (1) against the alternative of overdispersion using a function dispersiontest in R [31]. The result indicated there is no enough evidence to reject the null hypothesis (p-value: 0.99). This is probably because of excessive 0s in our data. We also compared the log-likelihoods of a negative binomial regression model and a Poisson regression model using a function
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odTest in R, and the result showed that a Poisson regression model performs better than a negative regression model [32]. Therefore, we choice to use the Poisson regression model. The overall fit of the Poisson regression model was tested using the residual deviance. The deviance chi-square goodness-of-fit
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test showed that the residual difference is small enough under the Poisson assumption, indicating that the model fits the data (p-value = 0.99).
Table 2 summarizes the results of the Poisson Regression model. The coefficients (β) indicate the change
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in the expected log count for a one-unit increase in continuous predictor variables and the expected log count between the test and reference group for categorical variables. The difference of two logs (log (xi) – log (xj)) is equal to the log of their ratio (log (xi /xj)). Therefore, we can interpret the model in terms of incidence-risk ratio (IRR) by taking exponentials of the coefficients (β). In addition, for the parameter estimates robust standard errors were adopted to adjust for mild violation of the assumption of Poisson distribution explained above.
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The results showed that the patient care assistant and therapist groups were the only HCW types that had a significant impact on the expected number of contacts with positive patients compared to the nurse group (=0.05). The patient care assistant and therapist groups are likely to contact positive patients 1.18
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and 1.28 times more than the reference group, holding other variables constant, respectively. However, the test for the overall effect of HCW role indicated that Prov_role is a statistically significant predictor
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of Num_cases (p-value < 0.05). The total number of patients (Num_pts) and average number of patients per day (Avg_PPD) were significant and had a negative association with the number of cases HCWs saw. If the total number of patients a HCW took care of during the two months increases by one patient, the expected contacts with positive patients decreased by a 0.7%. Similarly, for an increase in the average number of patients HCWs saw per LTACH workday, the expected number of contacts with positive cases decreases by 6.4%. On the other hand, for each additional LTACH workday a HCW it is 1.019 times more likely to see a positive
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patient.
The results also showed significant associations with HCW network measures. The expected number of positive contacts increased with increasing average degree centrality, when controlling for the number of
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patients and working days (IRR = 1.104). The average degree of centrality is increased when patients tend to have more connections within the network. The Poisson regression result indicates that a HCW has a greater chance of contacting positive patients if their patients are connected with more patients each day.
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Holding all other variables constant, a one percent increase in the average clustering coefficient increases the estimated positive contact by a factor of 1.658, but this was not statistically significant. The model also indicated that as the ratio of the number of actual connections between shared provider patients to the total possible connections increases by one unit the expected positive contacts decreases at the rate of 0.037. A network density of 100% indicates a perfectly dense network where all nodes are connected and a group of patients is continuously seen by the HCW over time. It is likely impractical to maintain this
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network long term. A HCW with a high network density might work fewer days in the LTACH, thus reducing the chance of contacting with CRE positives in the unit. 4. Discussion
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As the EMR system has been widely adopted, a large amount of patient and provider data is collected and stored in clinical data repositories. This big data has facilitated studies that predict patient risk and
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outcomes as well as system performances [33,34]. However, patient-provider relationships have primarily been studied through detailed observation with a small sample size. While these studies add more granularity to interactions, they are not feasible at a large scale considering task complexity of patients and providers [35]. Our study demonstrated how combined data from different sources can be utilized to assess the role of HCWs in transmitting HAIs between patients on a large scale.
We analyzed the data using Poisson regression. We anticipated HCW characteristics and patterns of interactions with patients would be associated with positive contact frequency. For example, certain
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provider roles are more likely to see positive patients (higher likelihood of transmitting infection than others). Among the six groups of HCWs who saw patients in the LTACH, therapists had the highest probability of interacting with cases, followed by patient care assistants. In fact, case managers had a
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higher chance of contacting cases than therapists, but its impact was not statistically significant. Also, case managers had an IRR greater than 1 with nurses being the comparator, but it was not significant. The small number of case managers and variability in the number of patients and working days for case managers may be reasons the result was not significant. Interestingly, HCWs who saw fewer patients per
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day during the LTACH workdays were more likely to encounter CRE positive patients. This is potentially due to the fact that HCWs involved in the care of CRE patients are often not able to take care of as many other patients due to the amount of care required by these sicker patients. If it is not possible to restrict the number of patients a HCW sees when one of their patients is CRE positive through cohorting, there could be more targeted interventions for personal protective equipment, hand hygiene, and sterilization of items
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HCWs wear, including ID badges. Also, given that frequency of contact with positives may be correlated with transmission to non-colonized patients, understanding the likelihood of contact with positive patients could influence staffing decisions and HCWs in need of targeted interventions to reduce colonization.
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Throughout this project there were many data challenges and lessons learned. Accessing provider data through EMR logs and culling the data to include only direct patient interaction was a painstaking process
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that required the aid of EMR information technology programmers and an infectious disease clinician. To allow for more accurate predictions of transmission dates, we chose a study period during which regular CRE perirectal screening was performed in high-risk units. Vetting data from the different sources, including patient medical records and colonization dates, was completed to ensure data integrity. While this process took time, more accurate results are possible using properly vetted data. A more systematic approach using decision rules may help determine relevant activities more efficiently and accurately. There were several limitations in this study. First, due to the limited time frame and hospital units where
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regular CRE screening occurred, our study had a small number of cases. The study was able to demonstrate how the modeling techniques combined with patient and provider datasets can be applied to understand HCW influence on infection transmission. The application of the techniques in larger data
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settings may enable a more accurate estimation of the degree of the transmission through HCWs. Second, clinicians were not able to be included as their interactions with patients were not reliably determined from the retrospective EMR data. While the exclusion of clinicians remains a limitation, we
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used provider data that included reliable information about tasks performed by HCWs who may have closer interactions with patients. Other technologies such as wearable devices tracking movement may help in the future to delineate interaction intensity and timing. Third, we excluded HCWs who saw only 1 patient per day. It may be possible they used the same clothes and badge through which infection could be still transmitted between two different days. However, the strength of connection between patients who shared HCWs on the same day and a next day would be
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different. Also, we think it is difficult to assume any behaviors of HCWs without directly interviewing or observing them. Therefore, we took a conservative approach in estimating transmission possibilities and only used same-day interactions.
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Fourth, patients admitted prior to the beginning of the two month period were included in the study, though we only included their data from dates after the start of the study. These patients would have had
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previous contact with HCWs where the infection may have taken place and that data was not recorded. Lastly, this study used day-level interactions that do not account for frequency of interactions throughout a day. In the provider data, it was not always clear if the time stamps of tasks involving patient interactions could be trusted. For patient safety and quality, it is recommended that HCWs document as close as possible to the actual event of care [36]. However, various factors including the complexity of a patient’s health problems and documenting locations (bedside vs. central station desks) affect timing and frequency of charting [37,38]. When provider data that includes more accurate time stamps of interactions
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is available, we will be able to consider the impact of frequency and interaction order on transmission risk.
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5. Conclusion
In summary, our study demonstrated the benefits of leveraging both patient and HCW interactions data from an ongoing CRE outbreak to understand the potential role of HCWs in HAI transmission. The model results indicated that working characteristics and patient network structures of HCWs are associated with
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the frequency of contacts with positive patients. If HCWs having a higher rate of contacts with positive patients are more likely to transmit infection between patients, understanding those provider-related features can help better examine the extent and pathways HCWs transmit HAI between patients. Furthermore, this understanding could allow healthcare institutions for valuable insight into the development of interventions to limit the transmission of HAIs through HCWs.
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6. Acknowledgements and Funding
This research was supported by the Coulter Translational Research Partnership and the University of Virginia Health Center.
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Tables: Table 1. Summary of measures for networks of 499 HCWs. Average degree
Average clustering
Average network
centrality
coefficient
density
pro of
Provider type Number of HCWs
279
5.15
0.79
0.72
Case Manager
20
8.92
0.99
0.77
Patient Care Assistant
92
8.70
0.92
0.76
Therapist
74
8.94
0.88
0.58
Specialty Technician
20
8.87
0.79
0.67
Other
14
0.79
0.69
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Nurses
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6.75
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Table 2. Poisson Regression Model Results.
Incident rate ratio Robust standard Coefficient (β)
pro of
[95% confidence error
p-value
interval]
Intercept
1.193
0.211
3.296 [2.178,4.987]
< 0.001
0.591
0.41
1.806 [0.809,4.033]
0.149
0.166
0.082
1.18 [1.005,1.386]
0.043
0.243
0.083
1.275 [1.084,1.5]
0.003
0.244
0.836 [0.519,1.348]
0.463
0.178
0.795 [0.561,1.126]
0.197
RoleType (Nurses) Case Manager Patient Care
Therapist Specialty
-0.179 Technician
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Assistant
-0.229
Num_pts
-0.007
0.002
0.993 [0.99,0.996]
< 0.001
Avg_PPD
-0.066
0.03
0.936 [0.883,0.993]
0.027
Work_days
0.019
0.003
1.019 [1.012,1.026]
< 0.001
0.099
0.011
1.104 [1.081,1.127]
< 0.001
0.506
0.273
1.658 [0.971,2.833]
0.064
-3.29
0.245
0.037 [0.023,0.060]
< 0.001
Avg_cls Net_den
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Avg_deg
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Other
Num_pts: total number of patients each provider saw during the two months; Avg_PPD: average patients each provider saw per day during LTACH workdays; Work_days: LTACH workdays;
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Avg_deg: average degree centrality; Avg_cls: average cluster coefficient; Net_den: network density
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Figure Captions: Figure 1. Diagram of the patient populations used in the analyses. (LTACH: long-term acute care hospital;
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HCWs: Healthcare Workers; np: number of patients; nh: number of HCWs; *: days HCWs worked in the LTACH included in the analysis)
Figure 2. Distribution of number of positive patients each HCW encountered during the period.
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Figure 3. Example HCW networks for (a) case manager, (b) therapist, (c) nurse, and (d) patient care assistant.
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2. All patients who stayed in the LTACH in July or August 2014 (np = 64)
3. HCWs who saw LTACH patients in July or August 2014 (nh = 542)
No
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Removed (nh = 43)
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1. All patients who stayed in the inpatient units or LTACH in July or August 2014
4. Patients who were seen by these HCWs in July or August 2014 (np = 3730)
5. Saw more than one patient per day during LTACH workdays?
Yes
6. HCWs included in the study* (nh = 499)
: patients : HCWs
7. Patients included for LTACH shared provider analysis (np = 1309)
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