Monitoring the spread of meticillin-resistant Staphylococcus aureus in The Netherlands from a reference laboratory perspective

Epidemiological cluster identification using multiple data sources: an approach using logistic regression

In the management of infectious disease outbreaks, grouping cases into clusters and understanding their underlying epidemiology are fundamental tasks. In genomic epidemiology, clusters are typically identified either using pathogen sequences alone or with sequences in combination with epidemiological data such as location and time of collection. However, it may not be feasible to culture and sequence all pathogen isolates, so sequence data may not be available for all cases. This presents challenges for identifying clusters and understanding epidemiology, because these cases may be important for transmission. Demographic, clinical and location data are likely to be available for unsequenced cases, and comprise partial information about their clustering. Here, we use statistical modelling to assign unsequenced cases to clusters already identified by genomic methods, assuming that a more direct method of linking individuals, such as contact tracing, is not available. We build our model on pairwise similarity between cases to predict whether cases cluster together, in contrast to using individual case data to predict the cases' clusters. We then develop methods that allow us to determine whether a pair of unsequenced cases are likely to cluster together, to group them into their most probable clusters, to identify which are most likely to be members of a specific (known) cluster, and to estimate the true size of a known cluster given a set of unsequenced cases. We apply our method to tuberculosis data from Valencia, Spain. Among other applications, we find that clustering can be predicted successfully using spatial distance between cases and whether nationality is the same. We can identify the correct cluster for an unsequenced case, among 38 possible clusters, with an accuracy of approximately 35 %, higher than both direct multinomial regression (17 %) and random selection (< 5 %).

A pseudo-outbreak of MRSA due to laboratory contamination related to MRSA carriage of a laboratory staff member

BACKGROUND

Methicillin resistant Staphylococcus aureus (MRSA) is a major burden for hospitals globally. However, in the Netherlands, the MRSA prevalence is relatively low due to the 'search and destroy' policy. Routine multiple-locus variable-number of tandem repeat analysis (MLVA) of MRSA isolates supports outbreak detection. However, whole genome multiple locus sequence typing (wgMLST) is superior to MLVA in identifying (pseudo-)outbreaks with MRSA. The present study describes a pseudo-outbreak of MRSA at the bacteriology laboratory of a large Dutch teaching hospital.

METHODS

All staff members of the bacteriology laboratory of the Elisabeth-TweeSteden hospital were screened for MRSA carriage, after a laboratory contamination with MRSA was suspected. Clonal relatedness between the index isolate and the MRSA isolates from laboratory staff members and all previous MRSA isolates from the Elisabeth-TweeSteden hospital with the same MLVA-type as the index case was examined based on wgMLST using whole genome sequencing.

RESULTS

One of the staff members was identified as the probable source of the laboratory contamination, because of carriage of a MRSA possessing the same MLVA-type as the index case. Eleven other isolates with the same molecular characteristics were found in the database, of which seven were retrospectively suspected of contamination. Clonal relatedness was found between ten isolates, including the isolate found in the staff member and the MRSA found in the index patient with a maximum of eleven alleles difference. All isolates were epidemiologically linked through the laboratory staff member, who had worked on all these cultures.

CONCLUSIONS

The present study describes a MRSA pseudo-outbreak over a 2.5-year period due to laboratory contamination caused by a MRSA carrying laboratory staff member involving nine patients. In case of unexpected bacteriological findings, the possibility of a laboratory contamination should be considered.

Universal risk assessment upon hospital admission for screening of carriage with multidrug-resistant micro-organisms in a Dutch tertiary care centre

BACKGROUND

In Dutch hospitals a six-point questionnaire is currently mandatory for risk assessment to identify carriers of multidrug-resistant organisms (MDROs) at the time of hospitalization. Presence of one or more risk factors is followed by pre-emptive isolation and microbiological culturing.

AIM

To evaluate the yield of the universal risk assessment in identifying MDRO carriers upon hospitalization.

METHODS

A cross-sectional study was performed using routine healthcare data in a Dutch tertiary hospital between January 1^st, 2015 and August 1^st, 2019. MDRO risk assessment upon hospitalization included assessment of: known MDRO carriage, previous hospitalization in another Dutch hospital during an outbreak or a foreign hospital, living in an asylum centre, exposure to livestock farming, and household membership of a meticillin-resistant Staphylococcus aureus carrier.

FINDINGS

In total, 144,051 admissions of 84,485 unique patients were included; 4480 (3.1%) admissions had a positive MDRO risk assessment. In 1516 (34%) admissions microbiological screening was performed, of which 341 (23%) yielded MDRO. Eighty-one patients were categorized as new MDRO carriers, as identified through MDRO risk assessment, reflecting 0.06% (95% confidence interval: 0.04-0.07) of all admissions and 1.8% (1.4-2.2) of those with positive risk assessment. As a result, the number of 'MDRO risk assessments needed to perform' and individual 'MDRO questions needed to ask' to detect one new MDRO carrier upon hospitalization were 1778 and 10,420, respectively.

CONCLUSION

The yield of the current strategy of MDRO risk assessment upon hospitalization is limited and it needs thorough reconsideration.

Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016

Introduction

With growing amounts of data available, identification of clusters of persons linked to each other by transmission of an infectious disease increasingly relies on automated algorithms. We propose cluster finding to be a two-step process: first, possible transmission clusters are identified using a cluster algorithm, second, the plausibility that the identified clusters represent genuine transmission clusters is evaluated.

Aim

To introduce visual tools to assess automatically identified clusters.

Methods

We developed tools to visualise: (i) clusters found in dimensions of time, geographical location and genetic data; (ii) nested sub-clusters within identified clusters; (iii) intra-cluster pairwise dissimilarities per dimension; (iv) intra-cluster correlation between dimensions. We applied our tools to notified mumps cases in the Netherlands with available disease onset date (January 2009 – June 2016), geographical information (location of residence), and pathogen sequence data (n = 112). We compared identified clusters to clusters reported by the Netherlands Early Warning Committee (NEWC).

Results

We identified five mumps clusters. Three clusters were considered plausible. One was questionable because, in phylogenetic analysis, genetic sequences related to it segregated in two groups. One was implausible with no smaller nested clusters, high intra-cluster dissimilarities on all dimensions, and low intra-cluster correlation between dimensions. The NEWC reports concurred with our findings: the plausible/questionable clusters corresponded to reported outbreaks; the implausible cluster did not.

Conclusion

Our tools for assessing automatically identified clusters allow outbreak investigators to rapidly spot plausible transmission clusters for mumps and other human-to-human transmissible diseases. This fast information processing potentially reduces workload.

Acetaminophen Use Concomitant with Long-Lasting Flucloxacillin Therapy: A Dangerous Combination

Acetaminophen and flucloxacillin both interfere with the γ-glutamyl cycle. Long-lasting concomitant use of flucloxacillin and acetaminophen can lead to 5-oxoproline accumulation and severe high anion gap metabolic acidosis. Females and patients with sepsis, impaired kidney and/or liver function, malnutrition, advanced age, congenital 5-oxoprolinase deficiency and supratherapeutic acetaminophen and flucloxacillin dosage are associated with increased risk. Therefore, a critical attitude towards the prescription of acetaminophen concomitant with flucloxacillin in these patients is needed.

We present the case of a 79-year-old woman with severe 5-oxoprolinaemia after long-lasting treatment with flucloxacillin and acetaminophen, explaining the toxicological mechanism and risk factors, and we make recommendations for acetaminophen use in patients with long-lasting flucloxacillin treatment.

Spread of Carbapenem-Resistant Klebsiella pneumoniae in Hub and Spoke Connected Health-Care Networks: A Case Study from Italy

The study describes the spread of carbapenem-resistant Klebsiella pneumoniae (CRKP) in a regional healthcare network in Italy. The project included several stages: (1) Establishment of a laboratory-based regional surveillance network, including all the acute care hospitals of the Marches Region (n = 20). (2) Adoption of a shared protocol for the surveillance of Multi-Drug Resistant Organisms (MDROs). Only the first CRKP isolate for each patient has been included in the surveillance in each hospital. The anonymous tracking of patients, and their subsequent microbial records within the hospital network, allowed detection of networks of inter-hospital exchange of CRKP and its comparison with transfer of patients within the hospital network. Pulsed-Field Gel Electrophoresis (PFGE) analysis has been used to study selected isolates belonging to different hospitals. 371,037 admitted patients have been included in the surveillance system. CRKP has shown an overall incidence rate of 41.0 per 100,000 days of stay (95% confidence interval, CI 38.5–43.5/100,000 DOS), a CRKP incidence rate of isolation in blood of 2.46/100,000 days of stay (95% CI 1.89–3.17/100,000 days of stay (DOS) has been registered; significant variability has been registered in facilities providing different levels of care. The network of CRKP patients’ exchange was correlated to that of the healthcare organization, with some inequalities and the identification of bridges in CRKP transfers. More than 73% of isolates were closely related. Patients’ exchange was an important route of spread of antimicrobial resistance, highlighting the pivotal role played by the hub, and selected institution to be used in prioritizing infection control efforts.

Frequent Undetected Ward-Based Methicillin-Resistant Staphylococcus aureus Transmission Linked to Patient Sharing Between Hospitals

Background

Recent evidence suggests that hospital transmission of methicillin-resistant Staphylococcus aureus (MRSA) is uncommon in UK centers that have implemented sustained infection control programs. We investigated whether a healthcare-network analysis could shed light on transmission paths currently sustaining MRSA levels in UK hospitals.

Methods

A cross-sectional observational study was performed in 2 National Health Service hospital groups and a general district hospital in Southeast London. All MRSA patients identified at inpatient, outpatient, and community settings between 1 November 2011 and 29 February 2012 were included. We identified genetically defined MRSA transmission clusters in individual hospitals and across the healthcare network, and examined genetic differentiation of sequence type (ST) 22 MRSA isolates within and between hospitals and inpatient or outpatient and community settings, as informed by average and median pairwise single-nucleotide polymorphisms (SNPs) and SNP-based proportions of nearly identical isolates.

Results

Two hundred forty-eight of 610 (40.7%) MRSA patients were linked in 90 transmission clusters, of which 27 spanned multiple hospitals. Analysis of a large 32 patient ST22-MRSA cluster showed that 26 of 32 patients (81.3%) had multiple contacts with one another during ward stays at any hospital. No residential, outpatient, or significant community healthcare contacts were identified. Genetic differentiation between ST22 MRSA inpatient isolates from different hospitals was less than between inpatient isolates from the same hospitals (P ≤ .01).

Conclusions

There is evidence of frequent ward-based transmission of MRSA brought about by frequent patient admissions to multiple hospitals. Limiting in-ward transmission requires sharing of MRSA status data between hospitals.

Epidemiology and control of meticillin-resistant Staphylococcus aureus in Stockholm County, Sweden, 2000 to 2016: overview of a "search-and-contain" strategy

To review the epidemiology and measures to control meticillin-resistant Staphylococcus aureus, MRSA, in Stockholm between 2000 and 2016 from the perspective of the Department of Communicable Disease Control and Prevention, Stockholm County Council, Sweden. Age, sex, and place of acquisition of their MRSA on all patients reported to the department were reviewed. Measures for control included surveillance through mandatory reporting of cases, screening patients with risk factors for MRSA, strict adherence to basic nursing hygienic principles, isolation of MRSA positive patients in single rooms in dedicated MRSA wards, and cohorting of staff. An MRSA team was created at the Department of Infectious Diseases, Karolinska University Hospital, for follow-up of all cases. Several administrative meetings and cooperative groups were formed that are still in function. From 2000 to 2016, there were 7373 MRSA cases reported. Healthcare-associated MRSA, HA-MRSA, was successfully controlled, and from 2006 onwards, very limited HA-MRSA transmission or outbreaks occurred. However, incidence increased overall, from 9.5 per 100,000 in 2000 to 37.3 per 100,000 in 2016, due to increase of MRSA acquired abroad and of MRSA acquired in the Swedish community. Surveillance and control measures have been successful in containing HA-MRSA in Stockholm, Sweden, but incidence has increased substantially due to imported cases and spread in the Swedish community. The strategy may be termed "search-and-contain" since screening, infection control, follow-up, and advice on personal hygiene were cornerstones of control, whereas eradication of carriage was not.

Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.