A statistical model to assess the risk of communicable diseases associated with multiple exposures in healthcare settings
1 Hospices Civils de Lyon, Service d’Hygiène, Epidémiologie et Prévention, Unité Epidémiologie et Biomarqueurs de l’Infection, F-69437, Lyon, France
2 CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Epidémiologie et Santé Publique, Université de Lyon; Université Lyon 1, F-69100, Villeurbanne, France
3 Hospices Civils de Lyon, Service de Biostatistique, F-69003, Lyon, France
BMC Medical Research Methodology 2013, 13:26 doi:10.1186/1471-2288-13-26Published: 20 February 2013
The occurrence of communicable diseases (CD) depends on exposure to contagious persons. The effects of exposure to CD are delayed in time and contagious persons remain contagious for several days during which their contagiousness varies. Moreover when multiple exposures occur, it is difficult to know which exposure is associated with the CD.
A statistical model at the individual level is presented to estimate the risk of CD to patients, in healthcare settings, with multiple observed exposures to other patients and healthcare workers and unobserved exposures to unobserved or unobservable sources. The model explores the delayed effect of observed exposure, of source contagiousness and of unobserved exposure. It was applied to data on influenza-like illness (ILI) among patients in a university hospital during 3 influenza seasons: from 2004 to 2007. Over a total of 138,411 patients-days of follow-up, 64 incident ILI cases were observed among 21,519 patients at risk of ILI.
The ILI risk per 10,000 patients-days associated with observed exposure was about 129.1 (95% Credible Interval (CrI): 84.5, 182.9) and was associated at 72% with exposures to patients or healthcare workers 1 day earlier and at 41% with the 1st day of source contagiousness. The ILI risk associated with unobserved exposure was 0.8 (95% CrI: 0.3, 1.6) per 10,000 patients-days in non-epidemic situation in the community and 4.3 (95% CrI: 0.4, 11.0) in epidemic situation.
The model could be an interesting epidemiological tool to further assess the relative contributions of observed and unobserved exposures to CD risk in healthcare settings.