Genomic survey of Clostridium difficile reservoirs in the East of England implicates environmental contamination of wastewater treatment plants by clinical lineages.

There is growing evidence that patients with Clostridiumdifficile-associated diarrhoea often acquire their infecting strain before hospital admission. Wastewater is known to be a potential source of surface water that is contaminated with C. difficile spores. Here, we describe a study that used genome sequencing to compare C. difficile isolated from multiple wastewater treatment plants across the East of England and from patients with clinical disease at a major hospital in the same region. We confirmed that C. difficile from 65 patients were highly diverse and that most cases were not linked to other active cases in the hospital. In total, 186 C. difficile isolates were isolated from effluent water obtained from 18 municipal treatment plants at the point of release into the environment. Whole genome comparisons of clinical and environmental isolates demonstrated highly related populations, and confirmed extensive release of toxigenic C. difficile into surface waters. An analysis based on multilocus sequence types (STs) identified 19 distinct STs in the clinical collection and 38 STs in the wastewater collection, with 13 of 44 STs common to both clinical and wastewater collections. Furthermore, we identified five pairs of highly similar isolates (≤2 SNPs different in the core genome) in clinical and wastewater collections. Strategies to control community acquisition should consider the need for bacterial control of treated wastewater.

Screening urine samples for the absence of urinary tract infection using the sediMAX automated microscopy analyser.

Urinalysis culminates in a workload skew within the clinical microbiology laboratory. Routine processing involves screening via manual microscopy or biochemical dipstick measurement, followed by culture for each sample. Despite this, as many as 80% of specimens are reported as negative; thus, there is vast wastage of resources and time, as well as delayed turnaround time of results as numerous negative cultures fulfil their required incubation time. Automation provides the potential for streamlining sample screening by efficiently (>30% sample exclusion) and reliably [negative predictive value (NPV) ≥ 95%] ruling out those likely to be negative, whilst also reducing resource usage and hands-on time. The present study explored this idea by using the sediMAX automated microscopy urinalysis platform. We prospectively collected and processed 1411 non-selected samples directly after routine laboratory processing. The results from this study showed multiple optimum cut-off values for microscopy. However, although optimum cut-off values permitted rule-out of 40.1% of specimens, an associated 87.5% NPV was lower than the acceptable limit of 95%. Sensitivity and specificity of leukocytes and bacteria in determining urinary tract infection was assessed by receiver operator characteristic curves with area under the curve values found to be 0.697 [95% confidence interval (CI): 0.665-0.729] and 0.587 (95% CI: 0.551-0.623), respectively. We suggested that the sediMAX was not suitable for use as a rule-out screen prior to culture and further validation work must be carried out before routine use of the analyser.