Background: Institutions such as hospitals, prisons, and long-term care facilities have been identified as focal points for the transmission of emerging infections. Cost-effective control of these infections in large populations requires the identification of optimal subpopulations for targeted infection control interventions. Our objective was to quantify and compare the relative impact that individual institutions or subpopulations have on wider population-level outbreaks of emerging pathogens.
Design: We describe a simple mathematical model to compute the epidemiologic weight (EW) of an institution or subpopulation. The EW represents the rate at which newly infectious individuals exit the institution under consideration.
Setting: A hypothetical academic tertiary-care hospital (700 beds, 5-day length of stay [LOS]) and prison (3098 inmates, 27-day LOS).
Patients: Individuals entering a hospital in-patient prison ward and nonprisoners entering both medical and surgical intensive-care units and those admitted to the general medical and surgical wards.
Results: The recent example of the community-acquired methicillin-resistant Staphylococcus aureus epidemic is used to illustrate the EW calculation. Hospitals and prisons, which often have vastly dissimilar populations sizes and LOSs and might have differing transmission rates, can have comparable EWs and thus contribute equally to an epidemic in the community.
Conclusions: This method highlights the importance of measuring entrance and exit colonization prevalences for the optimal targeting of prevention measures. The EW not only identified superspreader institutions but also ranks them, enabling public health workers to optimize the allocation of resources to places where they are likely to be of most benefit.