Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals.

INTRODUCTION: Outbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS. METHODS: Our AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013-2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system. RESULTS: During the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens' overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen. CONCLUSION: AODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments.

Incidence of healthcare-associated Clostridioides difficile infections and association with ward-level antibiotic consumption in a German university hospital: an ecological study.

OBJECTIVES: Clostridioides difficile infection (CDI) is one of the most important healthcare-associated infections. We aimed to describe the incidence density of healthcare-associated CDI (HA-CDI) in Germany's largest hospital and to identify associations with ward-level antimicrobial consumption. METHODS: We used surveillance data on CDI and antimicrobial consumption from 2014 to 2017 and analysed a potential association by means of multivariable regression analysis. RESULTS: We included 77 wards with 404998 admitted patients and 1850862 patient-days. Six hundred and seventy-one HA-CDI cases were identified, resulting in a pooled mean incidence density of 0.36/1000 patient-days (IQR = 0.34-0.39). HA-CDI incidence density on ICU and haematological-oncological wards was about three times higher than on surgical wards [incidence rate ratio (IRR) = 3.00 (95% CI = 1.96-4.60) and IRR = 2.78 (95% CI = 1.88-4.11), respectively]. Ward-level consumption of third-generation cephalosporins was the sole antimicrobial risk factor for HA-CDI. With each DDD/100 patient-days administered, a ward's HA-CDI incidence density increased by 2% [IRR = 1.02 (95% CI = 1.01-1.04)]. Other risk factors were contemporaneous community-associated CDI cases [IRR = 1.32 (95% CI = 1.07-1.63)] and CDI cases in the previous month [IRR = 1.27 (95% CI = 1.07-1.51)]. Furthermore, we found a significant decrease in HA-CDI in 2017 compared with 2014 [IRR = 0.68 (95% CI = 0.54-0.86)]. CONCLUSIONS: We confirmed that ward-level antimicrobial use influences HA-CDI and specifically identified third-generation cephalosporin consumption as a risk factor.