Hospital acquired infections and their investigation – dealing with the impact of time in hospital epidemiology

Abstract: The topic of this thesis is the investigation of hospital acquired infections (HAI) and how to deal with time dependency in this context. When investigating the risk of HAI, researchers face the challenge of competing risks. The complete picture can be illustrated and investigated via an illness-death model. Considering HAI as an outcome, there are two perspectives to distinguish amongst: The clinicians’ and the epidemiologists’ perspective, concerning risk and aetiology of HAI, respectively. A common approach for risk factor analysis of HAI is logistic regression. This is a valid but rather crude approach as time is ignored. This thesis examines the analysis of HAI via logistic regression adjusted for time, either length of stay
(LOS) or time at risk (TAR). Both approaches are attempts to incorporate time dependency. However, clarification is needed about what is modeled and whether this is appropriate. The investigation of adjustment for LOS demonstrates that this approach is not valid. LOS consists of the time before and the time after HAI. Thus, not only effects on the occurrence of HAI impact LOS, but also effects on death and discharge after the infection. A simulation study shows that risk factor analysis via logistic regression adjusted for LOS can result in misleading effect estimates leading to wrong conclusions. Furthermore, adjustment for TAR is investigated. While there are well understood regression models available for investigation
of the cumulative incidence function (CIF), insights are needed in what logistic regression adjusted for TAR models. A simulation study shows, that it is unclear how the results can be translated into a measure modeling the CIF and thus addressing the clinicians’ perspective. Furthermore, the results show that this approach is no measure corresponding to the epidemiologists’ perspective, distinguishing between direct and indirect effects on HAI.
A further challenge arising when investigating HAI are unknown infection times. It may occur that only the infection status and LOS are known, but the time of infection is not. This is an extreme version of interval censoring of time-to-event data. There are tools for interval censored data motivated by studies collecting data at scheduled visit times. Investigation via real and simulated data shows that these methods can be applied in an extreme interval censoring setting. The results for analysis of the clinicians’ and epidemiologists’ perspective are promising when considering HAI as an outcome. Considering HAI as a time-dependent exposure and its burden measured by change in LOS or its effect on the death and discharge hazard, estimates of each transition can be obtained from the existing tools for interval censored data. These estimates can be used for manual calculation of the quantity of interest. The topics covered in this thesis are important in recent hospital epidemiology. There are many settings where time dependency plays an important role but infection times are unknown. For instance, this is also the case when investigating COVID-19.
In conclusion, even with rough data it is worthwhile considering more sophisticated approaches in order to investigate HAI incorporating time. Thus, a better reflection of the reality of HAI development in can be given.₂

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Universität Freiburg, Dissertation, 2021

Klassifikation und Themen

• Schlagwort: Time ; Nosocomial infections ; Epidemiology ; Impact

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2021
• Urheber: Weber, Susanne
• Beteiligte Personen und Organisationen: Wolkewitz, Martin ; Ehl, Stephan ; Institut für Medizinische Biometrie und Statistik. Freiburg im Breisgau ; Albert-Ludwigs-Universität Freiburg. Medizinische Fakultät

Weitere Informationen

• DOI: 10.6094/UNIFR/222916
• URN: urn:nbn:de:bsz:25-freidok-2229160
• Rechteinformation: Kein Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:28 MESZ