We studied a dataset of care episodes in a regional Swedish hospital system. We followed how 2,314,477 patients moved between 8,507 units (hospital wards and outpatient clinics) over seven years. The data also included information on the date when patients tested positive with methicillin resistant Staphylococcus aureus. To simplify the complex flow of patients, we represented it as a network of units, where two units were connected if a patient moved from one unit to another, without visiting a third unit in between. From this network, we characterized the typical network position of units with a high prevalence of methicillin resistant Staphylococcus aureus, and how the patient’s location in the network changed upon testing positive. On average, units with medium values of the analyzed centrality measures had the highest average prevalence. We saw a weak effect of the hospital system’s response to the patient testing positive - after a positive test, the patient moved to units with a lower centrality measured as degree (i.e. number of links to other units) and in addition, the average duration of the care episodes became longer. The network of units was too random to be a strong predictor of the presence of methicillin resistantStaphylococcus aureus - would it be more regular, one could probably both identify and control outbreaks better. The migration of the positive patients with within the healthcare system, however, helps decreasing the outbreak sizes.
2014. Vol. 3, 29- p.
network epidemiology; methicillin resistant Staphylococcus aureus ; hospital system; healthcare associated infections