Enhancing the evaluation of pathogen transmission risk in a hospital by merging hand-hygiene compliance and contact data: a proof-of-concept study

TL;DR

This study demonstrates that combining hand-hygiene compliance data with contact patterns among healthcare workers provides a more comprehensive assessment of pathogen transmission risk in hospital settings, aiding outbreak investigation and modeling.

Contribution

It introduces a novel approach of merging hand-hygiene and contact data from wearable sensors to better evaluate transmission risks in hospitals.

Findings

Contact networks are highly structured with heterogeneous durations.

Low hand-hygiene compliance was observed among healthcare workers.

The combined data stratifies contacts into 'at-risk' and 'safe' categories.

Abstract

Hand-hygiene compliance and contacts of health-care workers largely determine the potential paths of pathogen transmission in hospital wards. We explored how the combination of data collected by two automated infrastructures based on wearable sensors and recording (i) use of hydro-alcoholic solution and (ii) contacts of health-care workers provide an enhanced view of the risk of transmission events in the ward. We perform a proof-of-concept observational study. Detailed data on contact patterns and hand-hygiene compliance of health-care workers were collected by wearable sensors over 12 days in an infectious disease unit of a hospital in Marseilles, France. 10837 contact events among 10 doctors, 4 nurses, 4 nurses' aids and 4 housekeeping staff were recorded during the study. Most contacts took place among medical doctors. Aggregate contact durations were highly heterogeneous and the resulting contact network was highly structured. 510 visits of health-care workers to patients' rooms were recorded, with a low rate of hand-hygiene compliance. Both data sets were used to construct histories and statistics of contacts informed by the use of hydro-alcoholic solution, or lack thereof, of the involved health-care workers. Hand-hygiene compliance data strongly enrich the information concerning contacts among health-care workers, by assigning a 'safe' or 'at-risk' value to each contact. The global contact network can thus be divided into 'at-risk' and 'safe' contact networks. The combined data could be of high relevance for outbreak investigation and to inform data-driven models of nosocomial disease spread.