The use of a Poisson hidden Markov model for automated detection of hospital outbreaks with vancomycin-resistant enterococci in routine surveillance data

Background: Despite the low prevalence of infections due to vancomycin-resistant enterococci (VRE) in the Netherlands, VRE is a frequent source of hospital outbreaks. We investigated whether a Poisson hidden Markov model (PHMM) can detect in-hospital VRE outbreaks in routine data from the Dutch Infectious Diseases Surveillance Information System for Antimicrobial Resistance (ISIS-AR). Methods: We performed a retrospective data linkage study from 2013 up to 2023, including data from 89 hospitals on VRE isolates from ISIS-AR. A PHMM was used to detect potential outbreaks based on weekly VRE counts at hospital level. Per week t, the model provides the probability P that the observed count arose from an outbreak. Thresholds of P(t) >0.5, P(t) >0.7, and P(t) >0.9 for at least two consecutive weeks were used. The PHMM's results were compared with outbreaks voluntarily reported to the ‘Early warning and response meeting on highly resistant microorganism outbreaks in healthcare institutes’. Detection percentages were calculated and VRE counts of reported but undetected outbreaks, and detected but unreported outbreaks were described. Findings: Of the 85 reported outbreaks, the model detected 87%, 86%, and 81% for thresholds P(t) >0.5, P(t) >0.7, and P(t) >0.9, respectively. Undetected outbreaks were mainly small outbreaks. The PHMM detected 66, 55, and 44 unreported potential outbreaks, respectively, with 44%, 35%, and 30% involving only one to two VRE-positive patients. Conclusion: Overall, the PHMM shows potential for detecting in-hospital VRE outbreaks in routine surveillance data, with high detection rates. A prospective study is needed for further optimization for clinical practice.