Are KISS data representative of German intensive care units? Statistical issues.
OBJECTIVE
Data collected within the German nosocomial infection surveillance system KISS are recommended as reference data for judging nosocomial infection rates in German intensive care units (ICUs). It is unknown whether the KISS data tend to under- or overestimate the true infection incidence rates. In this article, methodological aspects of the SIR1 study on the incidence of nosocomial infections are discussed, with the aim of estimating unbiased incidence rates of nosocomial infections in interdisciplinary German ICUs and examining whether the KISS data are representative.


METHODS
We discuss the following methodological issues: 1) Sample size estimation. 2) Stratified random sampling of German ICUs. 3) Investigation of seasonal effects. 4) Statistical modeling of incidence rates using a negative binomial regression model. 5) Comparison of weighted incidence rates with the standardized rate ratio (SRR).


RESULTS
Random sampling proved difficult to realize in practice since many ICUs refused to participate, particularly those in small hospitals. Analysis was adjusted for hospital size. No seasonal trends were found in the KISS data. Due to marked differences between ICUs, the number of infections is over-dispersed compared to a Poisson model, so negative binomial regression was used. Fifty ICUs were observed for two consecutive months each, corresponding to 21,832 patient days, during which 262 infections occurred. Infections were more frequent in large hospitals. The incidence rates provided by the SIR study are on average (SRR) 1.89 (1.63-2.20) times as large as those estimated by the KISS system.


CONCLUSIONS
For estimating nosocomial infection incidence rates, random sampling and statistical modeling of over-dispersion were successfully performed. The study provides evidence that the KISS surveillance system tends to underestimate the true incidence rates of nosocomial infections in German ICUs.
Rücker
G
G
Institute of Medical Biometry and Medical Informatics, Freiburg, Germany. ruecker@imbi.uni-freiburg.de
Schoop
R
R
Beyersmann
J
J
Schumacher
M
M
Zuschneid
I
I
D016010:Binomial Distribution; D003428:Cross Infection / Q000453:epidemiology*; D005858:Germany / Q000453:epidemiology; D019538:Health Care Surveys / Q000379:methods* / Q000706:statistics & numerical data; D006742:Hospital Bed Capacity; D006801:Humans; D015994:Incidence; D007362:Intensive Care Units / Q000592:standards / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D045808:Refusal to Participate; D018401:Sample Size; D012494:Sampling Studies