McBryde, E.S., Pettitt, A.N., Cooper, B.S., and McElwain, D.L.S. (2007) Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models. Journal of the Royal Society Interface, 4 (15). pp. 745-754.

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Abstract

Background: Antibiotic-resistant nosocomial pathogens can arise in epidemic clusters or sporadically. Genotyping is commonly used to distinguish epidemic from sporadic vancomycin-resistant enterococci (VRE). We compare this to a statistical method to determine the transmission characteristics of VRE.

Methods and findings: A structured continuous-time hidden Markov model (HMM) was developed. The hidden states were the number of VRE-colonized patients (both detected and undetected). The input for this study was weekly point-prevalence data; 157 weeks of VRE prevalence. We estimated two parameters: one to quantify the cross-transmission of VRE and the other to quantify the level of VRE colonization from sporadic sources. We compared the results to those obtained by concomitant genotyping and phenotyping.

We estimated that 89% of transmissions were due to ward cross-transmission while 11% were sporadic. Genotyping found that 90% had identical glycopeptide resistance genes and 84% were identical or nearly identical on pulsed-field gel electrophoresis (PFGE).

There was some evidence, based on model selection criteria, that the cross-transmission parameter changed throughout the study period. The model that allowed for a change in transmission just prior to the outbreak and again at the peak of the outbreak was superior to other models. This model estimated that cross-transmission increased at week 120 and declined after week 135, coinciding with environmental decontamination.

Significance: We found that HMMs can be applied to serial prevalence data to estimate the characteristics of acquisition of nosocomial pathogens and distinguish between epidemic and sporadic acquisition. This model was able to estimate transmission parameters despite imperfect detection of the organism. The results of this model were validated against PFGE and glycopeptide resistance genotype data and produced very similar results. Additionally, HMMs can provide information about unobserved events such as undetected colonization.

Item ID:	39787
Item Type:	Article (Research - C1)
ISSN:	1742-5662
Keywords:	HMM; nosocomial pathogens; genotyping; statistical modelling; VRE
Date Deposited:	13 Oct 2015 00:29
FoR Codes:	11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110309 Infectious Diseases @ 40% 11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111706 Epidemiology @ 40% 16 STUDIES IN HUMAN SOCIETY > 1605 Policy and Administration > 160508 Health Policy @ 20%
SEO Codes:	92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 60% 92 HEALTH > 9203 Indigenous Health > 920309 Pacific Peoples Health - Health System Performance (incl. Effectiveness of Interventions) @ 20% 92 HEALTH > 9202 Health and Support Services > 920207 Health Policy Evaluation @ 20%
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