Implementation of a delayed prescribing model to reduce antibiotic prescribing for suspected upper respiratory tract infections in a hospital outpatient department, Ghana

Background: High levels of antimicrobial resistance (AMR) in Ghana require the exploration of new approaches to optimise antimicrobial prescribing. This study aims to establish the feasibility of implementation of different delayed/back-up prescribing models on antimicrobial prescribing for upper respiratory tract infections (URTIs). Methods: This study was part of a quality improvement project at LEKMA Hospital, Ghana, (Dec 2019–Feb 2020). Patients meeting inclusion criteria were assigned to one of four groups (Group 0: No prescription given; Group 1; Patient received post-dated antibiotic prescription; Group 2: Offer of a rapid reassessment of patient by a nurse practitioner after 3 days; and Group 3: Post-dated prescription forwarded to hospital pharmacy). Patients were contacted 10 days afterwards to ascertain wellbeing and actions taken, and patients were asked rate the service on a Likert scale. Post-study informal discussions were conducted with hospital staff. Results: In total, 142 patients met inclusion criteria. Groups 0, 1, 2 and 3 had 61, 16, 44 and 21 patients, respectively. Common diagnosis was sore throat (73%). Only one patient took antibiotics after 3 days. Nearly all (141/142) patients were successfully contacted on day 10, and of these, 102 (72%) rated their experiences as good or very good. Informal discussions with staff revealed improved knowledge of AMR. Conclusions: Delayed/back-up prescribing can reduce antibiotic consumption amongst outpatient department patients with suspected URTIs. Delayed/back-up prescribing can be implemented safely in low and middle-income countries (LMICs)

Challenges of investigating a large food-borne norovirus outbreak across all branches of a restaurant group in the United Kingdom, October 2016

During October and November 2016, over 1,000 customers and staff reported gastroenteritis after eating at all 23 branches of a restaurant group in the United Kingdom. The outbreak coincided with a new menu launch and norovirus was identified as the causative agent. We conducted four retrospective cohort studies; one among all restaurant staff and three in customers at four branches. We investigated the dishes consumed, reviewed recipes, interviewed chefs and inspected restaurants to identify common ingredients and preparation methods for implicated dishes. Investigations were complicated by three public health agencies concurrently conducting multiple analytical studies, the complex menu with many shared constituent ingredients and the high media attention. The likely source was a contaminated batch of a nationally distributed ingredient, but analytical studies were unable to implicate a single ingredient. The most likely vehicle was a new chipotle chilli product imported from outside the European Union, that was used uncooked in the implicated dishes. This outbreak exemplifies the possibility of rapid spread of infectious agents within a restaurant supply chain, following introduction of a contaminated ingredient. It underlines the importance of appropriate risk assessments and control measures being in place, particularly for new ingredients and ready-to-eat foods

Data point embedding and cluster analysis for emm type 44.0. Keys as in Fig 7.

The analysis detects a compact cluster localised in the East of England in 2019 (rectangle), with its embedded points mapped to their geographical location by the arrows. Map created with Sf [55] using shapefiles from the GADM database (https://gadm.org/maps/GBR_1.html) and the Ordnance Survey Data Hub (https://osdatahub.os.uk/downloads/open/BoundaryLine).</p

Simulation experiment on realistic baseline.

A The total number of synthetic epidemic cases increases slowly from t = 40 until it reaches a peak at week t = 48, thus simulating the emergence of an outbreak. B The retrospectively-computed warning scores w(x) for the epidemic cases (red markers) are typically larger than those for the endemic cases (blue markers); plotting these vs time highlights the epidemic cluster. C Simulated outbreak points are close in both time and space, but it is hard to naively detect the anomaly (see also S2 and S4 Figs); localising the cases with w(x)>0.95 (orange markers) identifies the outbreak epicentre; in the inset, the true outbreak cases are marked with a red cross and the area detected by SaTScan is circled for comparison. Map created with Sf [55] using shapefiles from the GADM database (https://gadm.org/maps/GBR_1.html) and the Ordnance Survey Data Hub (https://osdatahub.os.uk/downloads/open/BoundaryLine). D Warning scores of two random replicates with different cylinder volumes are strongly correlated, showing that outbreak detection is robust with respect to the cylinder volume choice. E Timeliness: updating a true-case warning score prospectively as new cases are added shows that it increases as the outbreak progresses, thus permitting detection earlier than the peak time t = 48 (colour shades are case dates as in A).</p

Illustration of simulation data projected on a two-dimensional plane using t-distributed stochastic neighbour embedding (t-SNE).

Each simulated record is identified by a point of coordinates C1 and C2. The points are coloured by their warning scores ((A) 0 to 1, blue to red, highlighting the presence of a bright red cluster of points with high warning scores w>0.95) and by their record time (B), showing that t-SNE also preserves temporal proximity. (TIF)</p

Data point embedding for the common emm type 12.0.

No detected case is assigned a warning score higher than 0.95.</p