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

Curtis, Donald; Hlady, Christopher S.; Kanade, Gaurav; Pemmaraju, Sriram V.; Polgreen, Philip M.; Segre, Alberto M.

doi:10.1371/journal.pone.0079906

Cited by 31 publications

(37 citation statements)

References 45 publications

(46 reference statements)

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“…28 Understanding how healthcare workers move and interact with patients within hospitals can help inform both disease surveillance approaches and infection-control interventions. [29][30][31][32] Understanding how patients move throughout a state will help inform both disease surveillance approaches and infection-control interventions for a wide range of infections. For example, in our hospital graph, the clustering identified three distinct clusters, yet all the clusters are also linked by hospital transfers.…”

Section: Discussionmentioning

confidence: 99%

Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

Simmering

Polgreen

Campbell

et al. 2015

Infect. Control Hosp. Epidemiol.

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Objective To determine the effect on inter-hospital patient sharing via transfers on the rate of Clostridium difficile infections (CDI) in a hospital. Design Retrospective cohort Methods Using data from the Healthcare Cost and Utilization Project California State Inpatient Database, 2005–2011, we identified 2,752,639 transfers. We then constructed a series of networks detailing the connections formed by hospitals. We computed two measures of connectivity, indegree and weighted indegree, measuring the number of hospitals from which transfers into a hospital arrive, and the total number of incoming transfers, respectively. We estimated a multivariate model of CDI cases using the log-transformed network measures as well as covariates for hospital fixed effects, log median length of stay, log fraction of patients aged 65 or older, quarter and year indicators as predictors. Results We found an increase of one in the log indegree was associated with a 4.8% increase in incidence of CDI (95% CI: 2.3–7.4) and an increase of one in log weighted indegree was associated with a 3.3% increase in CDI incidence (95% CI: 1.5–5.2). Moreover, including measures of connectivity in the models greatly improved their fit. Conclusions Our results suggest infection control is not under the exclusive control of a given hospital but is also influenced by the connections and number of connections that hospitals have with other hospitals.

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Section: Discussionmentioning

confidence: 99%

Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

Simmering

Polgreen

Campbell

et al. 2015

Infect. Control Hosp. Epidemiol.

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“…More recently, Mastrandrea [26], studying a single infectious disease ward using radio frequency tracking devices, also found that physicians had the highest number of contacts with other health care workers, although this was within a total pool of only 22 HCWs. A study by Curtis et al [16], used movement patterns from electronic medical records to suggest that resident physicians and nurses had the most frequent HCW contacts. While this conflicts with other findings, their definition of a contact differs significantly and did not include contacts in areas where electronic medical records fail to capture.…”

Section: Discussionmentioning

confidence: 99%

“…These studies capture patient movement as it pertains explicitly to the more complex clinical services they receive but fail to capture HCW social or casual movement, such as visits to the cafeteria or meeting rooms, or some types of clinical contact (e.g. a second staff member assisting with patient mobilization or bathing, or cross-covering a colleague on break) [14][15][16]. Contact patterns for HCWs have been examined using radio frequency identification (RFID) tags, mote-based sensors, and direct observation [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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BackgroundNosocomial, or healthcare-associated infections (HAI), exact a high medical and financial toll on patients, healthcare workers, caretakers, and the health system. Interpersonal contact patterns play a large role in infectious disease spread, but little is known about the relationship between health care workers’ (HCW) movements and contact patterns within a heath care facility and HAI. Quantitatively capturing these patterns will aid in understanding the dynamics of HAI and may lead to more targeted and effective control strategies in the hospital setting.MethodsStaff at 3 urban university-based tertiary care hospitals in Canada completed a detailed questionnaire on demographics, interpersonal contacts, in-hospital movement, and infection prevention and control practices. Staff were divided into categories of administrative/support, nurses, physicians, and “Other HCWs” - a fourth distinct category, which excludes physicians and nurses. Using quantitative network modeling tools, we constructed the resulting HCW “co-location network” to illustrate contacts among different occupations and with locations in hospital settings.ResultsAmong 3048 respondents (response rate 38%) an average of 3.79, 3.69 and 3.88 floors were visited by each HCW each week in the 3 hospitals, with a standard deviation of 2.63, 1.74 and 2.08, respectively. Physicians reported the highest rate of direct patient contacts (> 20 patients/day) but the lowest rate of contacts with other HCWs; nurses had the most extended (> 20 min) periods of direct patient contact. “Other HCWs” had the most direct daily contact with all other HCWs. Physicians also reported significantly more locations visited per week than nurses, other HCW, or administrators; nurses visited the fewest. Public spaces such as the cafeteria had the most staff visits per week, but the least mean hours spent per visit. Inpatient settings had significantly more HCW interactions per week than outpatient settings.ConclusionsHCW contact patterns and spatial movement demonstrate significant heterogeneity by occupation. Control strategies that address this diversity among health care workers may be more effective than “one-strategy-fits-all” HAI prevention and control programs.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3093-x) contains supplementary material, which is available to authorized users.

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“…To examine patient mobility, we use graph theory, the mathematical analysis of networks that allows us to construct a network of movement how hospital units are connected by patient movement between them [6]. Network analysis has been previously used to examine intra-hospital transfers and ambulatory care [7,8,9], but limited studies of inter-hospital mobility [10,11]. Grouping patients by their location in a hospital gives us the opportunity to examine susceptibility and risk of CDI from a population perspective, and calculation of network centrality [6] provides us with context of how inpatient units are connected in our hospital via patient transfers.…”

Section: Introductionmentioning

confidence: 99%

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

Bush

Barbosa

Farooq

et al. 2018

Preprint

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With hospital-onset Clostridium difficile Infection (CDI) still a common occurrence in the U.S., this paper examines the relationship between unit-wide CDI susceptibility and inpatient mobility and creates a predictive measure of CDI called "Contagion Centrality". A mobility network was constructed using two years of patient electronic health record (EHR) data within a 739-bed hospital (Jan. 2013 -Dec. 2014; n=72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms and patient transfers between units (edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Closeness centrality was a statistically significant measure associated with unit susceptibility (p-value < 0.05), highlighting the importance of incoming patient mobility in CDI prevention at the unit-level. Contagion Centrality (CC) was calculated using incoming inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. This measure is statistically significant (p-value <0.05) with our outcome of hospital-onset CDI cases, and captures the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create an easily interpretable and informative clinical tool showing this relationship and risk of hospital-onset CDI in real-time. Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak, and thus provide clinicians and infection prevention staff with advanced warning and specific location data to concentrate prevention efforts.

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

Cited by 31 publications

References 45 publications

Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

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

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

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