title: An algorithm to estimate the importance of bacterial acquisition routes in hospital settings.
abstract: An algorithm is presented to calculate likelihoods of acquisition routes using only individual patient data concerning period of stay and microbiologic surveillance (without genotyping). The algorithm also produces estimates for the prevalence and the number of acquisitions by each route. The algorithm is applied to colonization data of third-generation cephalosporin-resistant Enterobacteriaceae (CRE) from September 2001 to May 2002 in two intensive care units (ICUs) (n = 277 and n = 180, respectively) of Utrecht, Kingdom of the Netherlands. Genotyping and epidemiologic linkage are used as the reference standard. Surveillance cultures were obtained on admission and twice weekly thereafter. All CREs were genotyped. According to the reference standard, the daily prevalence of CRE in ICU-1 and ICU-2 was 26.1% (standard deviation: 15.4) and 15.1% (standard deviation: 13.4), respectively, with five of 23 (21.7%) and six of 21 (28.6%) cases of acquired colonization being of exogenous origin, respectively. On the basis of the algorithm, the endogenous route was responsible for more acquisitions than the exogenous route (p = 0.003 and p < 0.001 for ICU-1 and ICU-2, respectively). The estimated number of acquisitions is 30 and 27, and the estimated prevalence is 27.6% and 17.6% for ICU-1 and ICU-2, respectively. By use of longitudinal colonization data only, the algorithm determines the relative importance of acquisition routes taking patient dependency into account.
authors.affiliation: Department of Mathematics, Utrecht University, Utrecht, Kingdom of the Netherlands. bootsma@math.uu.nl
authors.forename: M C J
authors.initials: MC
authors.lastname: Bootsma
authors.forename: M J M
authors.initials: MJ
authors.lastname: Bonten
authors.forename: S
authors.initials: S
authors.lastname: Nijssen
authors.forename: A C
authors.initials: AC
authors.lastname: Fluit
authors.forename: O
authors.initials: O
authors.lastname: Diekmann
mesh_terms: D000465:Algorithms; D002511:Cephalosporins / Q000494:pharmacology*; D003428:Cross Infection / Q000453:epidemiology* / Q000382:microbiology / Q000635:transmission; D024881:Drug Resistance, Bacterial; D004755:Enterobacteriaceae / Q000187:drug effects / Q000235:genetics*; D004756:Enterobacteriaceae Infections / Q000453:epidemiology* / Q000635:transmission; D005838:Genotype; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D008390:Markov Chains; D009426:Netherlands / Q000453:epidemiology; D018571:Sentinel Surveillance
chemical_list: D002511:Cephalosporins