Making Contact: Patterns of Transmission Help Healthcare Personnel Understand the Need for Infection Prevention Interventions

By Kelly M. Pyrek

Preventing the transmission of infectious agents is at the core of successful infection prevention and control practice, and knowing the methods in which a disease is transmitted is important for implementing proper infection control measures. Taking the methods of transmission (see adjacent sidebar) into consideration, it is important to be aware of contact patterns of transmission, which can assist healthcare professionals in understanding the dynamics of infectious diseases transmission and guide the design of infection control and prevention measures. As van Kleef, et al. (2013) acknowledge, "Transmission models have been used to understand complex systems and to predict the impact of control policies."

In their review on the topic, van Kleef (2013) found that MRSA was the most common bacterial species studied, followed by vancomycin-resistant Enterococcus (VRE), and, very occasionally, C. difficile. As the researchers explain, "The first model of HAI conceptualized the spread of antibiotic resistance in bacterial populations among hospital patients. This was soon followed by models evaluating the effectiveness of interventions to reduce antibiotic resistance (e.g. antibiotic cycling or mixing). Since then, most HAI models have aimed to quantify infection control effectiveness. The infection control measures most frequently considered among these have been: hand hygiene, patient isolation, HCW cohorting, antibiotic stewardship, and screening. Moreover, a wider variability of interventions has been evaluated in the later years."

Surveying pathogens throughout the healthcare setting is not enough; contact among healthcare workers in the hospital setting must be considered in the development of innovative approaches to infection prevention and control. English, et al. (2018) call for a better understanding of the interpersonal contact patterns that play a large role in infectious disease spread. The researchers say that quantitatively capturing these patterns will aid in understanding the dynamics of HAI and may lead to more targeted and effective control strategies in the hospital setting.

Babak Pourbohloul, PhD, adjunct professor at the Institute for Resources, Environment & Sustainability of the University of British Columbia, was a co-author of this aforementioned research, and explains the impetus for this study.

"During the spread of the severe acute respiratory syndrome (SARS) virus in 2003, Canada was one of the countries that was hit hard by SARS outbreaks," Pourbohloul says. "In the aftermath of these outbreaks, which predominantly affected hospital settings, a group of infectious disease physicians and hospital infection control specialists, public health decision-makers and mathematical modelers from across Canada came together to develop predictive risk assessment tools to improve Canada’s preparedness against potential spread of any future emerging infectious diseases (EIDs). The group recognized that new data should be collected from hospitals and new mathematical and computational tools should be developed to utilize these data to assess the extent to which an EID outbreak might impact healthcare settings. For over a decade, this collaboration has produced a broad range of outcomes from both methodological and application standpoints. The BMC paper was the first step in disseminating these results, where we provided a descriptive summary of what was observed through the survey. Subsequent steps were needed to study the transmission dynamics of infectious disease, perform hospital outbreak analysis and infer recommendations for intervention strategies. These issues are the subject of upcoming papers."

For the study, staff at three urban university-based tertiary-care hospitals in Canada completed a detailed questionnaire on demographics, interpersonal contacts, in-hospital movement, and infection prevention and control practices. Using quantitative network modeling tools, the researchers constructed a healthcare worker “co-location network” to illustrate contacts among different occupations and with locations in hospital settings. Among 3,048 respondents, an average of 3.79, 3.69 and 3.88 floors were visited by each healthcare worker each week in the three hospitals, with a standard deviation of 2.63, 1.74 and 2.08, respectively. Physicians reported the highest rate of direct patient contacts (more than 20 patients/day) but the lowest rate of contacts with other healthcare worker; nurses had the most extended (more than 20 minutes) periods of direct patient contact. The category of "other healthcare workers" had the most direct daily contact with all other healthcare personnel. Physicians also reported significantly more locations visited per week than nurses, other healthcare workers, or administrators; nurses visited the fewest. Public spaces such as the cafeteria had the most staff visits per week, but the least mean hours spent per visit. Inpatient settings had significantly more healthcare personnel interactions per week than outpatient settings.

"The outcomes summarized in the BMC paper [English, et al. (2018)] contain only the descriptive part of the study survey data," says Pourbohloul, who is also founder of and chief scientist at Complexiscope Consulting Inc. "The most illuminating point (not yet published) was that how the nonlinear interplay between heterogeneity in contact patterns and a pathogen’s contagiousness can impact optimal intervention strategies. In other words, while an intervention strategy might be very effective in one setting, the same intervention may not be as effective if implemented in another setting with a different contact network structure; therefore, site-specific control strategies that address the diversity among healthcare workers may be more effective than 'one-strategy-fits-all' HAI prevention and control programs."

English, et al. (2018) state that, "Understanding the movement and contact patterns of healthcare workers within hospital settings may allow for more targeted and effective infection control interventions.” In terms of understanding healthcare personnel contact patterns that can be applied directly to improving existing and introducing new infection control interventions, Pourbohloul says, "Depending on the route of transmission, and duration and proximity required for effective transmission of a pathogen between two individuals, different disease-specific contact networks may be inferred from the same actual daily/weekly contacts between individuals. For instance, the contact network responsible for the spread of droplet respiratory infections, may look different from the one responsible for the spread of aerosolized-respiratory infections, or the contact network for indirect routes of transmission."

He adds, " Any intervention strategy may be described/evaluated by changing the network structure (e.g., removal of nodes, or “rewiring” part of the contact network) and/or reduction (or elimination) of transmission along edges in the network. Combining these two features -- i.e., constructing disease-specific network from healthcare worker contact data, and evaluating different intervention strategies by changing network structure and disease transmissibility – may provide guidance to identify optimal control strategies tailored for each healthcare setting."

As we have seen, English, et al. (2018) found that physicians reported the highest number of direct patient contacts but the lowest number of contacts with other HCWs, while nurses had the most extended periods of direct patient contact. Pourbohloul emphasizes that healthcare professionals must remember that the number and duration of contacts impact the dynamics of infection transmission. While a thorough analysis of transmission dynamics was beyond the scope of this BMC paper, Pourbohloul says it will be the subject of a future publication.

As well, Pourbohloul says that the practical implementation of outcomes awaits the results of the researchers' second and third papers that will be forthcoming. He adds, "Providing reliable decision-support tools requires intensive mathematical and computational simulations. To ensure thorough sensitivity analysis of potential outbreak outcomes, we decided to publish the survey results (BMC paper) first, as a descriptive prelude to a more quantitative analysis. That said, the overall take-home message is that any state-of-the-art decision-support tool to inform policy should explicitly incorporate heterogeneity in contact patterns as well as type and nature of contact and transmission between individuals. While this type of high-resolution analysis may not have been envisioned even few years ago, advances in network science have now made such studies possible, which will aid decision makers to design site-specific solutions."

A Word About Healthcare Personnel and Community Contacts
Much discussion has centered on transmission in the healthcare setting, but it is imperative to consider the interaction between healthcare workers and members of the public when examining transmission patterns.

As English, et al. (2018) report, "Location analyses showed that public spaces, including the cafeteria, lobby café, and coffee shops, were visited the most frequently per week but for a relatively shorter duration of time; this finding highlights a potential vulnerability of non-clinical spaces in healthcare facilities to promote infection spread for moderately- to highly transmissible pathogens. Given the vast overlap of healthcare workers, patients and the general public that may simultaneously visit these areas, disease spread could easily be facilitated between otherwise unconnected wards or units (or the community at large). Targeting these high-traffic areas with interventions such as hand-hygiene (washing stations or alcohol-based sanitizers), or mask distribution, or facilitating spatial separation may be effective in reaching a large and diverse subset of the hospital population."

van Kleef (2013) say that mathematical models of HAIs have primarily been set in a single ward, with the intensive care unit (ICU) being the most frequent setting modeled, or a simplified hospital setting, lacking any further ward structure. They say that more recent studies, however, have incorporated the interaction between general wards and the ICU or between different wards. Van Kleef (2013) add, "Although these ward or hospital-based models do not usually treat the hospital as a closed system (i.e. hospital admission and discharge rates from and to a 'general community' are frequently included), transfer patterns between healthcare institutes are rarely considered, as are transmission dynamics within settings outside the healthcare facilities." 

Through a prospective survey of contacts through a self-reported diary, Jiang, et al. (2018) investigated healthcare workers' contacts during a work day and compare these against working adults from the general population. As the investigators note, "Healthcare workers (HCWs) may be the inadvertent interface between the healthcare setting and the community for such infections. The HCWs’ role as a vector for spreading pathogens to patients in the hospital setting is well recognized, and occupational infections among HCWs have been frequently documented, both for common pathogens circulating in the healthcare setting, as well as some newly emerged or re-emerging pathogens. HCWs may thus contribute to disease transmission from the hospital to the community and vice versa."

The researchers found that healthcare workers’ contacts differ substantially from those of community-based working adults and that healthcare personnel may thus be at higher risk of acquiring and spreading contact-transmissible and respiratory infections due to the nature of their work.

They discovered that healthcare workers and community-based working adults reported a total of 4,066 and 9,206 contacts respectively; 76.3 percent were work-related for the former compared to only 57.2 percent for the latter. In both groups, physical touch occurred with about half of all contacts. Persons they encountered daily contributed only 39.9 percent of healthcare workers’ contacts but 73.3 percent of community-based working adults’ contacts. For healthcare workers, about half the contacts lasted <15 min compared with <15 percent for community-based working adults; contacts aged under 20 years were 5.3 percent for the former and 14.3 percent for the latter, whereas contacts aged older than 60 years were 23.3 percent and 7.3 percent, respectively. A higher proportion of healthcare personnel's work-related contacts involved physical touch (49.5 percent versus 33.9 percent for community- based working adults). The proportion of contacts aged under 20 years was similar, but HCWs had proportionately much more exposure to contacts older than 60 years than community-based working adults.

According to the researchers, "Among the different HCW types, doctors reported the highest whereas ward-based nurses the lowest total work-related contacts. Although around half of ward-based and clinic-based nurses’ contacts involved physical touch, for the assorted HCWs, contacts involving physical touch were rare. Work-related contacts in clinic-based nurses, doctors, and assorted HCWs were relatively shorter than in ward-based nurses, with a substantial number effectively occurring with new contacts, i.e. persons they meet monthly or less frequently, or whom they never met before."

References:

English KM, Langley JM, McGeer A, Hupert N, Tellier R, Henry B, Halperin SA, Johnston L and Pourbohloul B. Contact among healthcare workers in the hospital setting: developing the evidence base for innovative approaches to infection control. BMC Infectious Diseases. 2018;18:184

Jiang L, et al. Infectious disease transmission: survey of contacts between hospital-based healthcare workers and working adults from the general population. Journal of Hospital Infection. 98 (2018) 404e411.

van Kleef E, et al. Modeling the transmission of healthcare associated infections: a systematic review. BMC Infectious Diseases.2013;13:294.