Development of a Novel Near-peer Surgical Simulation-based Teaching Programme for Intern Doctors

Background: Trends in healthcare have caused a shift in training towards more competency based programmes. The COVID-19 pandemic has reduced time available for direct exposure and clinical learning, necessitating incorporation of simulation in training. The objectives of this study were to develop, pilot and evaluate a four week simulation based surgical teaching programme. Method(s): Interns pursuing a career in Surgery joined a near-peer surgical training programme delivered by NCHDs. A survey established a baseline competency. Four skills workshops were delivered. Outcomes were measured using data from pre and post course surveys as well as a surgical skills competition. Result(s): Of The 12 trainees, 71% had scrubbed in theatre before. 50% were already confident to scrub independently, increased to 75% post training. 28% were confident gowning/gloving, increased to 75% post training. 28% were confident to place a Simple suture in theatre, this did not increase despite training. 42% were confident performing an instrument tie, increased to 75% post training. 14% were confident hand tying knots, this increased to 62%. 14% of participants were comfortable performing excisional biopsy in theatre, increased to 62% post training. Preparation and administration of local anaestetic could be performed confidently by 14% before training, this increased to 87%. on completion, a surgical skills competition showed that 100% were able to satisfactorily perform basic skills. Conclusion(s): Near-peer delivery of surgical training has enhanced The basic surgical skills of interns. Similar programmes in other sites would ensure that interns have The skills required to safely care for surgical patients.

An audit of the investigation and management of acute appendicitis at a tertiary referral centre in Ireland

Aim: Acute appendicitis (AA) is among The most common Emergency surgical presentations to Irish hospitals. In 2020, The World Society of Emergency Surgery (WSES) updated its Jerusalem guidelines for The investigation, diagnosis, and management of AA. We aimed to evaluate our institutional compliance with these guidelines in The COVID era and outline potential areas for improvement. Method(s): We performed a retrospective chart review of all patients admitted to our institution with The diagnosis of AA in July 2021 and compared them against The standards outlined in The WSES Jerusalem guidelines. Result(s): 39 patients were identified. Average age was 25.4 years. 2.6% (n=1) had clinical scores documented. 33.3% (n=13) had computerised tomography scans performed, 33.3% (n=13) underwent ultrasound scans and 33.3% (n=13) had no diagnostic imaging. 100% proceeded with surgical management. Average time to theatre was 16.9 hours. 23.1% (n=9) were open appendectomies, 74.4% (n=29) were laparoscopic appendectomies and 2.6% (n=1) was a laparoscopic converted to open appendectomy. 100% had histopathological analyses. 69.2% (n=27) were true AAs, 15.4% (n=6) were negative appendectomies and 15.4% (n=6) were non-inflamed appendices with other pathology including lymphoid hyperplasia, fecolith or enterobius. Average length of stay (AvLoS) was 3.6 days. Conclusion(s): As regards timely surgical intervention and routine histopathology, we are compliant with The guidelines. However increased utilisation of validated clinical scoring systems could potentially reduce negative appendectomy rates and AvLoS. We believe an educational intervention is required to improve our compliance with these standards.

Superspreading quantified from bursty epidemic trajectories.

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08-0.18), implying that 10 % of the infected cause between 70 % and 87 % of all infections.

Using crowd-sourced data for real-time monitoring of food prices during the COVID-19 pandemic: Insights from a pilot project in northern Nigeria.

The COVID-19 pandemic and related lockdown measures have disrupted food supply chains globally and caused threats to food security, especially in Sub-Saharan Africa. Yet detailed, localized, and timely data on food security threats are rarely available to guide targeted policy interventions. Based on real-time evidence from a pilot project in northern Nigeria, where food insecurity is severe, we illustrate how a digital crowdsourcing platform can provide validated real-time, high frequency, and spatially rich information on the evolution of commodity prices. Daily georeferenced price data of major food commodities were submitted by active volunteer citizens through a mobile phone data collection app and filtered through a stepwise quality control algorithm. We analyzed a total of 23,961 spatially distributed datapoints, contributed by 236 active volunteers, on the price of four commodities (local rice, Thailand rice, white maize and yellow maize) to assess the magnitude of price change over eleven weeks (week 20 to week 30) during and after the first COVID-related lockdown (year 2020), relative to the preceding year (2019). Results show that the retail price of maize (yellow and white) and rice (local and Thailand rice) increased on average by respectively 26% and 44% during this COVID-related period, compared to prices reported in the same period in 2019. GPS-tracked data showed that mobility and market access of active volunteers were reduced, travel-distance to market being 54% less in 2020 compared to 2019, and illustrates potential limitations on consumers who often seek lower pricing by accessing broader markets. Combining the price data with a spatial richness index grid derived from UN-FAO, this study shows the viability of a contactless data crowdsourcing system, backed by an automated quality control process, as a decision-support tool for rapid assessment of price-induced food insecurity risks, and to target interventions (e.g. COVID relief support) at the right time and location(s).

Variability of Individual Infectiousness Derived from Aggregate Statistics of COVID-19 (preprint)

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number R directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08 - 0.18), implying that 10 % of the infected cause between 70 % to 87 % of all infections.

Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation (preprint)

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20 % infecting more than 80 %, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.