Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics

Audrey Duval (1) , Thomas Obadia (2, 3) , Lucie Martinet (4) , Pierre-Yves Boëlle (5) , Eric Fleury (4) , Didier Guillemot (3, 1) , Lulla Opatowski (1) , Laura Temime (6, 7)

1 B2PHI - Biostatistique, Biomathématique, Pharmacoépidémiologie et Maladies Infectieuses
2 Bioinformatique évolutive - Evolutionary Bioinformatics
3 IP - Institut Pasteur [Paris]
4 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
5 Epidémiologie, Systèmes d'Information, Modélisation
6 MESuRS - Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires
7 PACRI - Pasteur-Cnam Risques infectieux et émergents

Understanding transmission routes of hospital-acquired infections (HAI) is key to improve their control. In this context, describing and analyzing dynamic inter-individual contact patterns in hospitals is essential. In this study, we used wearable sensors to detect Close Proximity Interactions (CPIs) among patients and hospital staff in a 200-bed long-term care facility over 4 months. First, the dynamic CPI data was described in terms of contact frequency and duration per individual status or activity and per ward. Second, we investigated the individual factors associated with high contact frequency or duration using generalized linear mixed-effect models to account for inter-ward heterogeneity. Hospital porters and physicians had the highest daily number of distinct contacts, making them more likely to disseminate HAI among individuals. Conversely, contact duration was highest between patients, with potential implications in terms of HAI acquisition risk. Contact patterns differed among hospital wards, reflecting varying care patterns depending on reason for hospitalization, with more frequent contacts in neurologic wards and fewer, longer contacts in geriatric wards. This study is the first to report proximity-sensing data informing on inter-individual contacts in long-term care settings. Our results should help better understand HAI spread, parameterize future mathematical models, and propose efficient control strategies.

Mots clés: Network topology; Health policy; Experimental models of disease; Epidemiology; Statistical methods

Domaines: Informatique mobile; Bio-Informatique, Biologie Systémique [q-bio.QM]

Audrey Duval, Thomas Obadia, Lucie Martinet, Pierre-Yves Boëlle, Eric Fleury, et al.. Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics. Scientific Reports, 2018, 8 (1), pp.1686. ⟨10.1038/s41598-018-20008-w⟩. ⟨hal-01696186⟩