Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors

Abstract

Background: Nosocomial infections place a substantial burden on health care systems and represent one of the major issues in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals.

Methods and findings: We used wearable active Radio-Frequency Identification Devices (RFID) to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants, with 51 health care workers, 37 patients, and 31 caregivers. Nearly 16,000 contacts were recorded during the study period, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections.

Conclusions: Our study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The obtained results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.

Figure 1. Schematic representation of detected contacts among 2 nurses (N1, N2) and 3 patients (P1, P2, P3) and corresponding measured quantities.

Each individual is represented by a node and a link corresponds to a contact established between two individuals. The width of the link is a measure of the duration of the contact, also indicated explicitly in terms of minutes. Multiple links can occur between two individuals, as highlighted in the pair N1-P1, indicating a contact of frequency larger than 1. The quantities introduced in the Materials and Methods section are calculated for the pair of individuals N1 and P1. The pair established one contact with frequency equal to two for a total duration of six minutes . By taking into account all interactions, individual N1 has established three contacts , two of which were distinct contacts , for a total duration of contacts equal to seven minutes .

Figure 2. Contact number and duration per role (A: ward assistants; D: Doctor; N: Nurse; P: Patients; C: Caregiver).

The plots show the median values per participant in each class of: the number of contacts (panel A), the number of distinct contacts (panel B), the cumulative time in contact (panel C). All quantities for a given class are computed on the contacts established by participants in that class with any other participant. Data are normalized to a 24-hour interval.

Figure 3. Probability density functions of the number of contacts per individual, (panel A), and of the cumulative time in contact (panel B).

Each plot corresponds to a given class and considers the contacts that an individual in that class established with any other individual. Contact duration is expressed in seconds and is normalized to a 24-hour interval.

Figure 4. Boxplots for the distributions of cumulative contact durations between individuals belonging to given role pairs (horizontal axis), given the occurrence of a contact.

Here we only consider non-zero values of , and contact durations are expressed in seconds and are normalized to a 24-hour interval. On normalizing, the experimental resolution of 20 seconds yields the lowest value of 2.5 seconds visible in the figure. As usual, the bottom and top of the boxes correspond to the 25^th and 75^th percentiles, and the horizontal segment indicates the median. The ends of the whiskers correspond to the 5^th and 95^th percentiles. The dots are outliers located outside the 90% confidence interval, i.e., events falling below the 5^th percentile or above the 95^th percentile.

Figure 5. Contact matrices defined on the classes of individuals.

Matrices are displayed for the number of contacts (panel A), the number of distinct contacts (panel B), and the cumulative time in contact (panel C). The matrix entry for classes X (row) and Y (column) is the median value of the node strengths for individuals of class X, computed on the contacts they had with individuals of class Y; the asymmetry of the matrices depends on the different numbers of individuals populating each class . Individuals of class X that did not have contacts with individuals of class Y count as nodes with zero strength, i.e., they affect the median value for the corresponding matrix entry. To increase the readability of the figure, matrix entries are grayscale-coded according to the median values, with the lightest and darkest shade of gray respectively corresponding to the minimum and maximum value for each matrix. Contact durations are expressed in minutes and normalized to a 24-hour interval.

Figure 6. Cumulative contact networks of individuals, for all pairs of classes and within each class.

Nodes represent unique individuals, and edges between nodes represent a cumulative face-to-face time over the whole monitoring period. In the off-diagonal layouts, nodes are positioned from left to right in increasing order of number of edges.