Research article

Individual movements and contact patterns in a Canadian long-term care facility

  • Received: 29 November 2017 Accepted: 07 May 2018 Published: 09 May 2018
  • 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.

    Citation: David Champredon, Mehdi Najafi, Marek Laskowski, Ayman Chit, Seyed M. Moghadas. Individual movements and contact patterns in a Canadian long-term care facility[J]. AIMS Public Health, 2018, 5(2): 111-121. doi: 10.3934/publichealth.2018.2.111

    Related Papers:

  • 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.
    加载中
    [1] Mcglone SM, Bailey RR, Zimmer SM, et al. (2012) The economic burden of Clostridium difficile. Clin Microbiol Infect 18: 282–289. doi: 10.1111/j.1469-0691.2011.03571.x
    [2] Cattuto C, Broeck WVD, Barrat A, et al. (2010) Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One 5: e11596. doi: 10.1371/journal.pone.0011596
    [3] Stehlé J, Voirin N, Barrat A, et al. (2011) High-resolution measurements of face-to-face contact patterns in a primary school. PLoS One 6: e23176. doi: 10.1371/journal.pone.0023176
    [4] Leecaster M, Toth DJA, Pettey WBP, et al. (2016) Estimates of social contact in a middle school based on self-report and wireless sensor data. PLoS One 1: e0153690.
    [5] Vanhems P, Barrat A, Cattuto C, et al. (2013) Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS One 8: e73970. doi: 10.1371/journal.pone.0073970
    [6] Obadia T, Silhol R, Opatowski L, et al. (2015) Detailed contact data and the dissemination of staphylococcus aureus in hospitals. PLoS Comput Biol 1: e1004170.
    [7] Génois M, Vestergaard CL, Fournet J, et al. (2015) Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Network Sci 3: 326–347. doi: 10.1017/nws.2015.10
    [8] Najafi M, Laskowski M, de Boer PT, et al. (2017) The effect of individual movements and interventions on the spread of influenza in long-term care facilities. Med Decis Making 37.
    [9] Saxena M, Gupta P, Jain BN (2008) Experimental analysis of RSSI-based location estimation in wireless sensor networks. Int Conf Commun Syst Software Middleware Workshops 2008: 503–510.
    [10] Moore D, Leonard J, Rus D, et al. (2004) Robust distributed network localization with noisy range measurements. 50–61.
    [11] Newman M (2010) Networks: An introduction. Astron Nachr 327: 741–743.
    [12] Toth DJ, Leecaster M, Pettey WB, et al. (2015) The role of heterogeneity in contact timing and duration in network models of influenza spread in schools. J R Soc Interface 12: 20150279. doi: 10.1098/rsif.2015.0279
    [13] Lloydsmith JO, Schreiber SJ, Kopp PE, et al. (2005) Superspreading and the effect of individual variation on disease emergence. Nature 438: 355–359. doi: 10.1038/nature04153

    © 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
  • publichealth-05-02-111-s01.pdf
  • Reader Comments
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(865) PDF downloads(702) Cited by(2)

Article outline

Figures and Tables

Figures(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog