It has long been observed that interpersonal contacts with an ability to transmit a disease are not random as assumed by simple models of infectious disease spreading. If it is possible to estimate the characteristics of such a non-random network of contacts between individuals, we could improve the predictive and explanatory power of epidemic models. There are not so many pathogens, however, that spread over pathways where the network structure can be estimated. For this to be possible, contacts with the capacity to transmit the disease need to be discernable among all different types of inter-individual contacts, so that a network of effective contacts can be faithfully constructed. This is the case for e.g. sexually transmitted infections [
1] and - the topic of this paper - healthcare associated infections (HAI) [
2].
The first network-epidemiological study of the spread of disease in healthcare systems is, to our knowledge, Meyers
et al. [
3]. In this work, the authors model contagion between units populated by immobile patients. The model assumes the disease to spread between units by medical staff acting as vectors [
4], [
5] and is used to argue for the key-role of the staff in the spreading dynamics. Karkada
et al. [
6] and Lee
et al. [
7] make similar simulation-based studies concluding that patient transfer in critical care and nursing homes, respectively, are important factors in the dynamics of HAIs. Liljeros
et al. [
8] investigated a subset of the dataset we use in this paper. This smaller dataset recorded 295,108 inpatients from the Stockholm area of Sweden over two years. Liljeros
et al. focused mostly on methodological questions, such as how to represent this dataset as a network of patients that is as relevant for investigating disease spreading as possible. The authors argue that different diseases need different network representations depending on their route of transmission. Ueno and Masuda [
9] investigate a dataset from Tokyo community hospital sampling 388 patients and 217 doctors and nurses. They simulate disease transmission in this data and evaluate different strategies for controlling epidemics. Vanhems
et al. [
10] use a data set of similar size acquired from wearable sensors (detecting when patients or health-care workers are within a range of 1–1.5 m). They find a very heterogeneous contact structure where some health-care workers are much more central in the contact network than others. Hornbeck
et al. [
11] use a very similar data set to reach very similar conclusions. Donker
et al. [
12], [
13] study a large dataset of patient flow between hospitals within the Netherlands. Their data is aggregated on a coarser level than ours - a node in the network is a hospital - but it does cover an entire nation. Donker
et al. find a directionality of the flow towards larger, academic hospitals. This could, they argue, be exploited to control the transmission of healthcare associated pathogens (in Ref. [
14] they make this point stronger by simulations and argue that just reversing the patient flow would reduce the HAI prevalence dramatically). The final network-epidemiological study of HAI we are aware of is Walker
et al.’s study of
Clostridium difficile in inpatients of the Oxfordshire region of the United Kingdom [
15]. In this paper, the authors retrace possible transmission trees among 1,282 positive cases. They find that about 25% of the cases can be explained by an infection within the hospital system.
Currently researchers have, as seen above, either studied smaller, high precision data recorded by electronic sensors or large-scale patient referral data. These two types of data have their pros and cons - with high precision data could perhaps identify singular infection events, on the other hand, an epidemic outbreak is a large-scale phenomenon that is affected by the large-scale contact structure that at present can only be studied by patient referral data. The present paper investigated a dataset of the large-scale category.
1We use a record of all care episodes in the Stockholm region, making it possible to map the patient flow between units (that could be either a hospital ward or an outpatient clinic), we also knew who tested positive with methicillin-resistant
Staphylococcus aureus (MRSA) - an important nosocomial pathogen - and when they tested positive. However, we did not (like Ueno and Masuda [
9]) have records of the movement of the medical staff. We had to assume that the transmission of MRSA could take place outside the dataset (i.e. a patient could be infected in the community outside the healthcare system). One interesting question is how to infer these missing chains, which implicitly would mean how one can predict the false negative patients within the records of the regional healthcare system. For our data, and the methods we can envision, this would give too uncertain results at an individual level. We would have to aggregate the results to make meaningful observations. In this work, we do not take such an individual-level approach and integrate the results. Rather, we study the system at an intermediate level - the level of health-care units. We represented the hospital system as network of units. Briefly stated, we linked two units A and B if a patient had care episodes in both units without having been admitted in any other unit in between. The links between units thus capture the possibility of infection spreading from one unit to another (or in terms of newly infected patients the link, or course, represents certainty). Just like the topology of the contact network can help us to better understand how the contact patterns between individuals affect disease transmission (which individuals that are most influential, how influential they are relative to the average, how a disease can most efficiently be mitigated, etc.) [
1], [
16], [
17], a network of units can teach us about how the organization of the hospital system affects disease spreading. There has recently been a debate in the literature of the of the benefits of screening patients for MRSA (see Refs. [
18], [
19] and further references therein). A more cost effective alternative to screening all patients would be to, guided by analyses like the ones in this paper, focus on high-risk units.