title: Optimal screening strategies for healthcare associated infections in a multi-institutional setting.
abstract: Health institutions may choose to screen newly admitted patients for the presence of disease in order to reduce disease prevalence within the institution. Screening is costly, and institutions must judiciously choose which patients they wish to screen based on the dynamics of disease transmission. Since potentially infected patients move between different health institutions, the screening and treatment decisions of one institution will affect the optimal decisions of others; an institution might choose to "free-ride" off the screening and treatment decisions of neighboring institutions. We develop a theoretical model of the strategic decision problem facing a health care institution choosing to screen newly admitted patients. The model incorporates an SIS compartmental model of disease transmission into a game theoretic model of strategic decision-making. Using this setup, we are able to analyze how optimal screening is influenced by disease parameters, such as the efficacy of treatment, the disease recovery rate and the movement of patients. We find that the optimal screening level is lower for diseases that have more effective treatments. Our model also allows us to analyze how the optimal screening level varies with the number of decision makers involved in the screening process. We show that when institutions are more autonomous in selecting whom to screen, they will choose to screen at a lower rate than when screening decisions are more centralized. Results also suggest that centralized screening decisions have a greater impact on disease prevalence when the availability or efficacy of treatment is low. Our model provides insight into the factors one should consider when choosing whether to set a mandated screening policy. We find that screening mandates set at a centralized level (i.e. state or national) will have a greater impact on the control of infectious disease.
authors.affiliation: Department of Pharmacy Practice & Science, University of Iowa College of Pharmacy, Iowa City, Iowa, United States of America.
authors.forename: Aaron C
authors.initials: AC
authors.lastname: Miller
authors.affiliation: Department of Pharmacy Practice & Science, University of Iowa College of Pharmacy, Iowa City, Iowa, United States of America.
authors.forename: Linnea A
authors.initials: LA
authors.lastname: Polgreen
authors.affiliation: Division of Infectious Diseases, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States of America.
authors.forename: Philip M
authors.initials: PM
authors.lastname: Polgreen
mesh_terms: D000465:Algorithms; D003140:Communicable Disease Control; D003141:Communicable Diseases / Q000175:diagnosis* / Q000453:epidemiology; D019295:Computational Biology; D003428:Cross Infection / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control; D017048:Health Care Costs; D006801:Humans; D008403:Mass Screening; D008490:Medical Informatics; D018803:Models, Economic; D015999:Multivariate Analysis; D015995:Prevalence; D012680:Sensitivity and Specificity