Digital epidemiology encompasses an emerging set of analytic techniques and approaches to data collection. Data in these studies are almost always born digital -- they are not recorded or transcribed by hand -- and often the research involves online networks in one guise or another. While these methods are being utilized increasingly, studies combining both digital network data and microbiological data on the spread of hospital associated pathogens have, so far as I know, been missing.
Obadia et al have published an exemplary study doing just this for the case of MRSA and MSSA in a long term care center. Many researchers have in the past adopted a very reasonable and plausible hypothesis regarding the spread of staph in hospitals: namely, that it depends to a large extent upon person to person contact. If that's true, then obviously the ways in which patients and healthcare workers (HCWs) interact with one another, i.e., the patient-patient and patient-HCW contact networks, must be important for understanding spread. To my knowledge, until this study, nobody has really documented this with clarity at the individual level.
Obadia et al have illustrated this relationship between staph infection and contact network structure quite clearly by utilizing wireless proximity sensing and spa typing. They demonstrate how to employ digital technology to measure who interacts with whom, how frequently, and for how long, over long periods of time, and how to combine that data with microbiological surveillance in order to observe how transmission depends on the web of contacts in a facility. The authors found that close proximity interaction (CPI) paths existed between those colonized with like staph strains, and that those path lengths were significantly shorter than paths between random pairs in the study population. This is in agreement with what is expected from the transmission hypothesis. Their study also highlighted the importance of HCWs as links in the chain of contacts between infected patients.
One important implication of this work is that it might be possible to prevent infections by managing and monitoring close contact paths between patients and patients and between HCWs and patients. The approach may also be useful for developing targeted surveillance strategies that can detect spread and break the contact pathways most likely to result in further spread. I recommend reading the paper, and also the excellent comments regarding it by Eli Perencevich at the Controversies in Hospital Infection Prevention blog.
Overall, I think this study is a great illustration of the power of digital epidemiology methods for gathering detailed data in order to understand how disease is spread in the real -- as opposed to the simplified, theoretical -- world. We need more like it to inform both our thinking about hospital associated infections and analytic models of such pathogens.
(image source: CDC)