Background: The CDC recommends routine use of contact precautions for patients infected or colonized with multidrug-resistant organisms (MDROs). There is variability in implementation of and adherence to this recommendation, which we hypothesized may have been exacerbated by the COVID-19 pandemic. Methods: In September 2022, we emailed an 8-question survey to Emerging Infections Network (EIN) physician members with infection prevention and hospital epidemiology responsibilities. The survey asked about the respondent’s primary hospital’s recommendations on transmission-based precautions, adjunctive measures to reduce MDRO transmission, and changes that occurred during the COVID-19 pandemic. We sent 2 reminder emails over a 1-month period. We used descriptive statistics to summarize the data and to compare results to a similar EIN survey (n = 336) administered in 2014 (Russell D, et al. doi:10.1017/ice.2015.246). Results: Of 708 EIN members, 283 (40%) responded to the survey, and 201 were involved in infection prevention. Most respondents were adult infectious diseases physicians (n = 228, 80%) with at least 15 years of experience (n = 174, 63%). Respondents were well distributed among community, academic, and nonuniversity teaching facilities (Table 1). Most respondents reported that their facility routinely used CP for methicillin-resistant Staphylococcus aureus (MRSA, 66%) and vancomycin-resistant Enterococcus (VRE, 69%), compared to 93% and 92% respectively, in the 2014 survey. Nearly all (>90%) reported using contact precautions for Candida auris, carbapenem-resistant Enterobacterales (CRE), and carbapenem-resistant Acinetobacter spp, but there was variability in the use of contact precautions for carbapenem-resistant Pseudomonas aeruginosa and extended-spectrum β-lactamase–producing gram-negative organisms. In 2014, 81% reported that their hospital performed active surveillance testing for MRSA, and in 2022 this rate fell to 54% (Table 2). The duration of contact precautions varied by MDRO (Table 3). Compared to 2014, in 2022 facilities were less likely to use contact precautions indefinitely for MRSA (18% vs 6%) and VRE (31% vs 11%). Also, 180 facilities (90%) performed chlorhexidine bathing in at least some inpatients and 106 facilities (53%) used ultraviolet light or hydrogen peroxide vapor disinfection at discharge in some rooms. Furthermore, 89 facilities (44%) reported institutional changes to contact precautions policies after the start of the COVID-19 pandemic that remain in place. Conclusions: Use of contact precautions for patients with MDROs is heterogenous, and policies vary based on the organism. Although most hospitals still routinely use contact precautions for MRSA and VRE, this practice has declined substantially since 2014. Changes in contact-precaution policies may have been influenced by the COVID-19 pandemic, and more specifically, contemporary public health guidance is needed to define who requires contact precautions and for what duration.

Disclosures: None

Background: Candida auris, an emerging fungal pathogen, is frequently drug resistant and spreads rapidly in healthcare facilities. Screening to identify patients colonized with C. auris can prevent further spread by prompting aggressive infection prevention and control measures. The CDC recommends C. auris screening based on local epidemiological conditions, patient characteristics, and facility-level risk factors; such screening might help facilities in higher burden areas to mitigate transmission and those in lower-burden areas to detect new introductions before spread begins. To describe US screening practices and challenges, we surveyed a network of infection disease practitioners, comparing responses by local C. auris case burdens. Methods: In August 2022, we emailed a survey about C. auris screening practices to ~3,000 members of the IDSA Emerging Infection Network. We describe survey results, stratifying findings by whether the healthcare facility was in a region where C. auris is frequently identified (tier 3 facility) or not frequently identified (tier 2 facility), based on CDC assessment using existing multidrug-resistant organism containment guidance (https://www.cdc.gov/hai/containment/guidelines.html). Results: We received 253 responses (tier 3 facilities: 119, tier 2 facilities: 134); overall, 37% performed screening. Tier 3 facilities more frequently performed screening than tier 2 facilities (59% vs 17%). Among facilities that performed screening, tier 3 facilities, compared with tier 2 facilities, more frequently screened patients on admission (84% vs 55%) and used an in-house laboratory for testing (68% vs 29%), most often with culture-based methods. Tier 2 facilities more frequently screened patients already admitted in the facility (eg, in response to cases or as part of point-prevalence surveys) compared with tier 3 facilities (59% vs 49%). Among facilities performing screening, 72% had identified ≥1 case in the previous year (tier 3 facilities, 85%; tier 2 facilities, 33%). Barriers to screening included limited laboratory capacity, long testing turnaround times, and the perception that screening was not useful. Conclusions: Most facilities surveyed did not perform C. auris screening. However, most facilities that performed screening, including those in regions of higher and lower C. auris burden, detected cases during the previous year. Admission screening, which might help detect new introductions before spread begins, was uncommon in facilities in lower-burden areas. Improving ease of C. auris screening through access to in-house laboratory testing with rapid turnaround times might increase the adoption of C. auris screening by facilities, thereby increasing detection and preventing spread.

Disclosures: None

The purpose of this document is to highlight practical recommendations to assist acute-care hospitals in prioritization and implementation of strategies to prevent healthcare-associated infections through hand hygiene. This document updates the Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals through Hand Hygiene, published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology (SHEA). It is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America, the Association for Professionals in Infection Control and Epidemiology, the American Hospital Association, and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.

Previous studies have suggested that a hospital patient's risk of developing healthcare facility-onset (HCFO) Clostridioides difficile infections (CDIs) increases with the number of concurrent spatially proximate patients with CDI, termed CDI pressure. However, these studies were performed either in a single institution or in a single state with a very coarse measure of concurrence. We conducted a retrospective case-control study involving over 17.5 million inpatient visits across 700 hospitals in eight US states. We built a weighted, directed network connecting overlapping inpatient visits to measure facility-level CDI pressure. We then matched HCFO-CDIs with non-CDI controls on facility, comorbidities and demographics and performed a conditional logistic regression to determine the odds of developing HCFO-CDI given the number of coincident patient visits with CDI. On average, cases' visits coincided with 9.2 CDI cases, which for an individual with an average length of stay corresponded to an estimated 17.7% (95% CI 12.9–22.7%) increase in the odds of acquiring HCFO-CDI compared to an inpatient visit without concurrent CDI cases or fully isolated from both direct and indirect risks from concurrent CDI cases. These results suggest that, either directly or indirectly, hospital patients with CDI lead to CDIs in non-infected patients with temporally overlapping visits.

Pediatric antimicrobial stewardship programs (ASPs) improve antibiotic use for hospitalized children. Prescriber surveys indicate acceptance of ASPs, but data on infectious diseases (ID) physician opinions of ASPs are lacking. We conducted a survey of pediatric ID physicians, ASP and non-ASP, and their perceptions of ASP practices and outcomes.

We performed a survey of adult infectious diseases (ID) physicians to explore unintended consequences of antimicrobial stewardship programs (ASP). ID physicians worried about disagreement with colleagues, provider autonomy, and remote recommendations. Non-ASP ID physicians expressed more concern regarding ASPs focus on costs, provider efficiency, and unintended consequences of ASP guidance.

We surveyed infectious disease specialists about early coronavirus disease 2019 (COVID-19) vaccination preparedness. Almost all responding institutions rated their facility’s preparedness plan as either excellent or adequate. Vaccine hesitancy and concern about adverse reactions were the most commonly anticipated barriers to COVID-19 vaccination. Only 60% believed that COVID-19 vaccination should be mandatory.

Background: The movement of healthcare professionals (HCPs) induces an indirect contact network: touching a patient or the environment in one area, then again elsewhere, can spread healthcare-associated pathogens from 1 patient to another. Thus, understanding HCP movement is vital to calibrating mathematical models of healthcare-associated infections. Because long-term care facilities (LTCFs) are an important locus of transmission and have been understudied relative to hospitals, we developed a system for measuring contact patterns specifically within an LTCF. Methods: To measure HCP movement patterns, we used badges (credit-card–sized, programmable, battery-powered devices with wireless proximity sensors) worn by HCPs and placed in 30 locations for 3 days. Each badge broadcasts a brief message every 8 seconds. When received by other badges within range, the recipients recorded the time, source badge identifier, and signal strength. By fusing the data collected by all badges with a facility map, we estimated when and for how long each HCP was in any of the locations where instruments had been installed. Results: Combining the messages captured by all of our devices, we calculated the dwell time for each job type (eg, nurses, nursing assistants, physical therapists) in different locations (eg, resident rooms, dining areas, nurses stations, hallways, etc). Although dwell times over all job and area types averaged ∼100 seconds, the standard deviation was large (115 seconds), with a mean of maximums by job type of ∼450 seconds. For example, nursing assistants spent substantially more time in resident rooms and transitioned across rooms at a much higher rate. Overall, each distribution exhibits a power-law–like characteristic. By aggregating the data from devices with location data extracted from the floor plan, we were able to produce an explicit trace for each individual (identified only by job type) for each day and to compute cross-table transition probabilities by area for each job type. Conclusions: We developed a portable system for measuring contact patterns in long-term care settings. Our results confirm that frequent interactions between HCPs and LTC residents occur, but they are not uniform across job types or resident locations. The data produced by our system can be used to better calibrate mathematical models of pathogen spread in LTCs. Moreover, our system can be easily and quickly deployed to any healthcare settings to similarly inform outbreak investigations.

Funding: None

Disclosures: Scott Fridkin reports that his spouse receives a consulting fee from the vaccine industry.

Background:Clostridioides difficile infections (CDIs) present and are transmitted in both community and healthcare settings. Patients who become colonized or infected during hospitalization may be discharged into the community. Asymptomatic spread and/or community-based transmission have also been posited as alternative sources for healthcare-onset CDI cases. The objective of our study was to determine whether individuals are at greater risk for developing a CDI if they have a family member that spent time hospitalized in the prior 90 days, even if the hospitalized family member had no prior diagnosis of CDI. Methods: We conducted a retrospective cohort study using the Truven Marketscan database from 2001 through 2017; both commercial claims and Medicare supplemental data were included. We categorized enrollees by age, sex, month, year, exposure to a family member with CDI, hospitalization, or high- or low-risk antibiotic use in the prior 90 days. We then subdivided these groups based on the total amount of time that other family members spent hospitalized in the prior 90 days: ≤4 days, 5–10, 11–20, 21–30, 41–50 or >50 days. Within each subgroup, we computed the incidence of CDI. We then used a stratified regression model (log-linear quasi-Poisson) to estimate the incidence of CDI in each enrollment bin. Finally, we repeated our analysis using all CDI cases, CDI cases with no prior CDI in the family, and cases without prior hospitalization. Results: Over the 17-year study period, >5.1 billion enrollment months were represented in our dataset. We identified 224,818 cases of CDI, 223,744 cases without prior CDI in a family member and 164,650 CDI cases where the case patient had no prior hospitalization. Table 1 depicts the estimated risk (incident rate ratios) associated with the amount of time that other family members spent hospitalized in the prior 90 days. There is a very clear dose–response curve, and the relative risk for CDI increase as the amount of time other family members spent hospitalized increased. Other risk factors included prior hospitalization, low- and high-risk antibiotics, age, female sex and exposure to a family member with CDI. Conclusions: Having a family member who has been hospitalized in the prior 90 days significantly increases the risk for CDI, even if the family member did not have CDI. The total amount of time other family members spent in the hospital is positively associated with the level of risk.

Funding: CDC Modeling Infectious Diseases (MInD) in Healthcare Network

Disclosures: None

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.

Funding: None

Disclosures: None

Background: Certain nursing home (NH) resident care tasks have a higher risk for multidrug-resistant organisms (MDRO) transfer to healthcare personnel (HCP), which can result in transmission to residents if HCPs fail to perform recommended infection prevention practices. However, data on HCP-resident interactions are limited and do not account for intrafacility practice variation. Understanding differences in interactions, by HCP role and unit, is important for informing MDRO prevention strategies in NHs. Methods: In 2019, we conducted serial intercept interviews; each HCP was interviewed 6–7 times for the duration of a unit’s dayshift at 20 NHs in 7 states. The next day, staff on a second unit within the facility were interviewed during the dayshift. HCP on 38 units were interviewed to identify healthcare personnel (HCP)–resident care patterns. All unit staff were eligible for interviews, including certified nursing assistants (CNAs), nurses, physical or occupational therapists, physicians, midlevel practitioners, and respiratory therapists. HCP were asked to list which residents they had cared for (within resident rooms or common areas) since the prior interview. Respondents selected from 14 care tasks. We classified units into 1 of 4 types: long-term, mixed, short stay or rehabilitation, or ventilator or skilled nursing. Interactions were classified based on the risk of HCP contamination after task performance. We compared proportions of interactions associated with each HCP role and performed clustered linear regression to determine the effect of unit type and HCP role on the number of unique task types performed per interaction. Results: Intercept-interviews described 7,050 interactions and 13,843 care tasks. Except in ventilator or skilled nursing units, CNAs have the greatest proportion of care interactions (interfacility range, 50%–60%) (Fig. 1). In ventilator and skilled nursing units, interactions are evenly shared between CNAs and nurses (43% and 47%, respectively). On average, CNAs in ventilator and skilled nursing units perform the most unique task types (2.5 task types per interaction, Fig. 2) compared to other unit types (P < .05). Compared to CNAs, most other HCP types had significantly fewer task types (0.6–1.4 task types per interaction, P < .001). Across all facilities, 45.6% of interactions included tasks that were higher-risk for HCP contamination (eg, transferring, wound and device care, Fig. 3). Conclusions: Focusing infection prevention education efforts on CNAs may be most efficient for preventing MDRO transmission within NH because CNAs have the most HCP–resident interactions and complete more tasks per visit. Studies of HCP-resident interactions are critical to improving understanding of transmission mechanisms as well as target MDRO prevention interventions.

Funding: Centers for Disease Control and Prevention (grant no. U01CK000555-01-00)

Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)

Background. Colonization pressure at the unit level is known to be a risk factor for Clostridioides difficile infections in hospitals. Because C. difficile colonization is not routinely detected in clinical practice, only patients identified as having C. difficile infection (CDI) are included in these pressure calculations. We used data from the University of Iowa Hospitals and Clinics (UIHC) to determine whether highly local CDI pressure, due to patients in nearby rooms, is more strongly correlated with CDI than unit-level CDI pressure. Methods: We designed a base logistic regression model using variables known to be risk factors for CDI: age, antibiotic/gastric acid suppressor use, low albumin, prior hospitalization, comorbidities. To the base model, we add 2 measures, mean colonization pressure (MCP) and sum colonization pressure (SCP) of CDI at the unit level to obtain new models. To the base model, we also added CDI colonization pressure by considering CDI cases at different distance thresholds from the focal patient. Distances between patient rooms were extracted from hospital floor plans. Results: Adding unit-level CDI colonization pressures to the base model improved performance. However, adding CDI colonization pressures due to roommates and due to patients at different distances improved the model much more (Table 1). The top (resp. bottom) row shows in-sample (resp. out-of-sample) C-statistics for the base model, the base model with unit-level MCP, the base model with roommate MCP, and the base model with MCP from patients are different distances added as separate features. C-statistics for the base model and the base model with unit CDI pressure (SCP and MCP) are compared in Fig. 1 with C-statistics from the base model with CDI pressure from patients at distances D = 0, 1, 2, 3, 4, 5, 10, 15, 20 hops (1 hop = 5–6 meters). Conclusions: Our results support the hypothesis that unit CDI colonization pressure is a risk factor for CDI. However, by incorporating spatially granular notions of distances between patients in our analysis, we were able to demonstrate that the true source of CDI pressure at the UIHC is almost exclusively attributable to roommates and patients in adjacent rooms.

Funding: None

Disclosures: None

Background: Patient sharing between hospitals has long been known to be a contributor to the regional transmission of hospital-acquired infections. This inter–healthcare-facility connectedness suggests that regional, as opposed to local, control and surveillance strategies should be favored. However, the absence of easily and universally accessible patient transfer data hampers researchers and public health agencies who wish to build accurate network models of interfacility transmissions. Medicare data offer only a biased subsample of the full patient transfer network, but because it is widely available, this data source has historically been used for inference and simulation studies. Objective: The purpose of this study was to determine whether Medicare data could successfully be recalibrated to more closely resemble 100% inpatient capture data. Methods: We used data from the Healthcare Cost and Utilization Project (HCUP) to construct 100% capture and Medicare-only patient sharing networks among hospitals in Iowa, Wisconsin, and Nebraska. We used matrix decomposition techniques on the Medicare-only networks along with hospital characteristics from the American Hospital Association (AHA) data for feature construction in a truncated Poisson regression model, and we used Monte Carlo integration to obtain predicted values. These predicted values served as calibrated Medicare-only networks. We split the patient transfer data into training and testing sets and computed the mean squared prediction error (MSPE) for the testing data. We also built an individual based model (IBM) using HCUP and AHA data to perform epidemic simulations that depended on a matrix of patient transfer rates between hospitals. We then compared epicurves from these IBMs resulting from 100% capture networks, Medicare-only networks, and our calibrated networks. Results: Our calibrated networks reduced the MSPE with respect to Medicare-only networks by 84%, 47%, and 88% for Iowa, Wisconsin, and Nebraska, respectively. Although the epicurves from Medicare-only networks differed considerably from that from 100% capture networks, our calibrated networks retained high fidelity to the curves obtained from 100% capture networks. Conclusions: Medicare-only networks greatly underestimate the number of patients transferred between hospitals. Our approach allows us to use Medicare data to estimate networks when 100% inpatient capture is unavailable.

Funding: None

Disclosures: None

Background:Clostridioides difficile is a leading cause of healthcare-associated infections, and greater healthcare exposure is a primary risk factor for Clostridioides difficile infection (CDI). Longer hospital stays and greater CDI pressure, both at the hospital level and the level, have been linked to greater risk. In addition, symptoms associated with healthcare-associated CDI often do not present until a patient has been discharged. Our study objective was to estimate the extent to which exposure to different types of healthcare settings (eg, prior hospitalization, emergency department [ED], outpatient or long-term care) increase risk for hospital-onset CDI. Methods: We conducted a case-control study using the Truven Marketscan Commerical Claims and Medicare Supplemental databases from 2001 to 2017. Case patients were selected as all inpatient visits with a secondary diagnosis of CDI and no previous CDI diagnosis in the prior 90 days. Controls were selected from all inpatient admissions without any CDI diagnosis during the current admission or prior 90 days. A logistic regression model was used to estimate risk associated with prior healthcare exposure. Indicators were created for prior exposure to different healthcare settings: separate indicators were used to indicate transfer, exposure to that setting in the prior 1–30 days, 31–60 days and 61–90 days. Separate indicators were created for prior hospitalization, ED, outpatient clinic, nursing home or long-term care facilities (LTCFs), psychiatric or substance-abuse facility or other outpatient facility. We also included an indicator for prior exposure to a family member with CDI and prior outpatient antibiotics. Results: Estimates for selected variables (odds ratios) are presented in Table 1. Prior hospitalization, ED visits, outpatient clinics, nursing home and LTCFs were all associated with increased risk of secondary diagnosed CDI. Prior hospitalization and nursing home/LTCF conveyed the greatest risk. In addition, a ‘dose-–response’ relationship occurred for each of these exposure settings, with exposure nearest the admission date having the largest risk. Prior exposure to psychiatric , substance abuse, or other outpatient facilities were not risk factors for CDI. Having a family member with prior CDI and both low-risk and high-risk outpatient antibiotics were associated with increased risk. These factors also exhibited a ‘dose–response’ pattern. Conclusions: Exposure to various healthcare settings significantly increased risk for secondary CDI. Prior healthcare exposures occurring nearest to the point of admission conveyed the greatest risk. These results suggest that many hospital-associated CDI cases attributed to a current hospital stay may actually be acquired from prior healthcare settings.

Funding: CDC Modeling Infectious Diseases (MInD) in Healthcare Network

Disclosures: None

Background: Hospital-acquired infections are commonly spread through the movement of healthcare professionals (HCPs). Computational simulations provide a powerful tool for understanding how HCP behavior contributes to these infections, but how well they reflect the real world rests on a number of critical parameters. Our goal is to provide accurate, fine-grained estimates of real HCP movement and interaction parameters suitable for simulating the potential spread of pathogens over different types of inpatient facilities. Methods: We obtained a commercial data set with 44 million deidentified elements compiled from >27,000 HCPs from >30 job types. The data were collected over 27 months from >20 facilities of varying size using a proprietary electronic sensor system. Each observation recorded an HCP visiting 1 of 12,000 rooms (38% being patient rooms) and consisted of the entry and exit time stamps, hand hygiene behavior, and for many rooms, their (x, y) geometric coordinates within the facility. From these data, we can reconstruct the behavior (including location and hand-hygiene adherence) of each instrumented HCP across multiple shifts. Results: Distributions describing various aspects of HCP behavior (eg, arrival rates and dwell times) were derived using HCP job function, department or unit assignment, type of shift (day vs night), time of day, facility size, and staffing of facility. In a similar fashion, we constructed HCP cross-table transition probabilities using job type, room type, department type, unit type, and facility type. These distributions were used to generate reasonable HCP movement and behavior patterns in a simulation environment. Distributions of dwell time were, for the most part, heavy tailed, but they varied by type of job and facility: dwell times over all facilities, job types, and room types averaged ∼339 seconds (SD, 495 seconds), with a mean of maximums by job type of ∼37,168 seconds. However, these distributions differ within job type but across facilities (ie, nurses in 1 facility averaged 397 seconds, but 277 seconds in another) and within facility but across job type. For example, physicians averaged 292 seconds, whereas nurses averaged 397 seconds and physical therapists averaged 861 seconds. Conclusions: Our results provide a unique resource for disease modelers who wish to build meaningful simulations of the transmission of hospital-acquired infections. The scale and diversity of the data gave us the unique capability to provide, with confidence, distinct parameter sets for different types and sizes of healthcare facilities across a wide range of situations.

Funding: None

Disclosures: None

Background: In the field of public health, network models are useful for understanding the spread of both information and infectious diseases. Collecting network data requires determining network boundaries (ie, the entities selected for data collection). These decisions, if not made carefully, have potential outsized downstream effects on study findings. In practice, collaboration and coordination between healthcare organizations are often dictated by historical or geopolitical boundaries (eg, state or county boundaries), which may distort the underlying network under study, and thereby affect the reliability and/or accuracy of the network model. Objective: We compared natural communities in a patient-sharing network with those induced by geopolitical boundaries. Methods: Using data from the Healthcare Cost and Utilization Project (HCUP), we constructed a patient-sharing network among hospitals in California, splitting the data into a training set and a holdout set. We performed edge-betweenness clustering on the training set, and with the holdout set, we compared the resulting partition with the partition by counties using modularity. We also clustered contiguous counties that might function more cohesively together than individually. We performed spatially constrained hierarchical clustering on the network constructed from total patient flow between pairs of counties. The results were again compared via modularity on the holdout set to the county partition. Lastly, we built an individual-based model (IBM) using HCUP and American Hospital Association data to perform epidemic simulations. For each of several counties, we implemented this model to estimate the proportion of patients infected over time. We then reran the individual-based model using the entire state while dividing the results into corresponding counties. Results: In total, 680,485 patients transferred between 374 hospitals in 55 counties from 2003 to 2011. The out-of-sample modularity for the edge-betweenness clustering partition was 464% higher than that of the county partition. Aggregating the counties into half as many contiguous clusters was 319% higher, and aggregating them into 6 clusters was 489% higher (Fig. 1). The epicurves from the individual-based model ranged from small to significant deviations between state versus county boundaries (Fig. 2) . Conclusions: Collecting network data using externally imposed boundaries may lead to inaccurate network models. For example, counties serve as a poor proxy for their underlying communities, resulting in poor overall disease spread simulation results when county boundaries are allowed to drive network construction. These issues should be considered when building coordination partnerships such as the Accountable Communities for Health.

Funding: None

Disclosures: None

Background: Mobility patterns of healthcare workers (HCWs) (ie, the spatiotemporal distribution of patient rooms they visit) have a significant impact on the spread of healthcare acquired infections (HAIs). Objective: In this project, we used fine-grained data from a sensor deployment at the medical intensive care unit (MICU) in the University of Iowa Hospitals and Clinics (UIHC) to study the mobility patterns of HCWs and their impact on HAI spread. Methods: We analyzed 10 days of data from a 20-bed MICU sensor deployment. For parameters t1 and t2, each pair of rooms i and j is assigned a weight W(i, j) representing the number of times an HCW spends at least t1 seconds in room i followed by at least t1 seconds in room j, within t2 seconds of each other. W(i, j) is a measure of HCW traffic going from room i to room j; we study the correlation between W(i, j) and the distance between rooms i and j. Additionally, we perform 2 disease-spread simulations: (1) a base simulation, obtained by replaying observed HCW mobility traces and (2) a perturbed simulation, which is the same as the base simulation, except that we replace each HCW who visits a room by a random available HCW. Thus, the perturbed simulation removes correlations in the observed HCW mobility traces. Results: We computed W(i, j) for all room pairs i, j for parameters t1 = 30 seconds and t2 = 1,800 and 3,600 seconds. For nurses, there was a strong negative correlation of between pairwise room distance and the weights W(i, j) (−0.768 for t2 = 1,800; −0.711 for t2 = 3,600), The more distant 2 rooms were, the less they shared nurse traffic. This was not true for physicians (correlation = −0.027 for t2 = 1,800; −0.014 for t2 = 3,600). Figure 1 shows a weight versus distance scatter plot for nurses for t1 = 30 and t2 = 1,800. This spatial correlation has positive implications for disease spread; the base simulation, which preserves these spatial correlations, has between 12% and 55% fewer mean infected patients (>100 replicates) for different simulation parameters compared to the perturbed simulation. Conclusions: Our results, based on fine-grained data, show a “naturally emerging” cohorting behavior of nurses, where nurses are more likely to visit rooms close to each other within a 30–60 minute time window, than rooms further away. Through simulations, this behavior provides substantial protection against disease spread.

Funding: None

Disclosures: None