title: A stochastic model to measure patient effects stemming from hospital-acquired infections.
abstract: We introduce a Markov chain model to represent a patient's path in terms of the number and type of infections s/he may have acquired during a hospitalization period. The model allows for categories of patient diagnosis, surgery, the four major types of nosocomial (hospital-acquired) infections, and discharge or death. Data from a national medical records survey including 58,647 patients enable us to estimate transition probabilities and, ultimately, perform statistical tests of fit, including a validation test. Novel parameterizations (functions of the transition matrix) are introduced to answer research questions on time-dependent infection rates, time to discharge or death as a function of patient diagnostic groups and conditional infection rates reflecting intervening variables (e.g., surgery).
mesh_terms: D003428:Cross Infection / Q000453:epidemiology*; D003953:Diagnosis-Related Groups; D006784:Hospitals, Teaching / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay; D008390:Markov Chains; D008962:Models, Theoretical; D011336:Probability; D013269:Stochastic Processes; D014481:United States