Abstract
Contact networks of individuals in healthcare facilities are poorly understood, largely due to the lack of spatio-temporal movement data. A better understanding of such networks of interactions can help improve disease control strategies for nosocomial outbreaks. We sought to determine the spatio-temporal patterns of interactions between individuals using movement data collected in the largest veterans long-term care facility in Canada. We processed close-range contact data generated by the exchange of ultra-low-power radio signals, in a prescribed proximity, between wireless sensors worn by the participants over a two-week period. Statistical analyses of contact and movement data were conducted. We found a clear dichotomy in the contact network and movement patterns between residents and healthcare workers (HCWs) in this facility. Overall, residents tend to have significantly more distinct contacts with the mean of 17.3 (s.d. 3.6) contacts, versus 3.5 (s.d. 2.3) for HCWs (p-value < 10−12), for a longer duration of time (with mean contact duration of 8 minutes for resident-resident pair versus 4.6 minutes for HCW-resident pair) while being less mobile than HCWs. Analysis of movement data and clustering coefficient of the hourly aggregated network indicates that the contact network is loosely connected (mean clustering coefficient: 0.25, interquartile range 0–0.40), while being highly structured. Our findings bring quantitative insights regarding the contact network and movements in a long-term care facility, which are highly relevant to infer direct human-to-human and indirect (i.e., via the environment) disease transmission processes. This data-driven quantification is essential for validating disease dynamic models, as well as decision analytic methods to inform control strategies for nosocomial infections.
Highlights
Infectious diseases inflict a significant health and economic burden in hospitals and long-term care facilities
Our analysis presented here is based on the second period of data collection, which was used to parameterize an agent-based model of influenza transmission dynamics in the long-term care facility (LTCF) and evaluate the effect of different intervention strategies [8]
We observed a clear dichotomy between residents and healthcare workers (HCWs) with regards to the number of distinct contacts
Summary
Infectious diseases inflict a significant health and economic burden in hospitals and long-term care facilities. Nosocomial infections (e.g., influenza, pneumonia, gastrointestinal illness, urinary tract infections) have enormous impact on healthcare systems in terms of costs and patient outcomes, incurring billions of dollars every year only in the developed world [1]. Controlling outbreaks in these population settings is challenging, as the source of infection is often unknown. Possible congregation during daily activities creates an environment for repeated exposures to infection, which can facilitate disease spread in vulnerable individuals with underlying health conditions.
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