Background Following concerns about increased antibiotic use during the COVID-19 pandemic, trends in community antibiotic prescriptions in Scotland were evaluated. Methods The primary care prescription electronic messaging system used in GP practices with NHS contracts provided near real-time data analysis of national data. The main outcome measures were the weekly number of prescriptions for antibiotics generated by prescribers in GP practices in 2020 compared with 2019. Results At end of Week 12 2020 (22 March), after a sharp increase, the number of prescriptions commonly used for respiratory infections was 44% higher than the corresponding week in 2019. The number of prescriptions for respiratory antibiotics reduced through April and May 2020, with 34% fewer prescriptions issued by end of Week 22 (31 May) than in the corresponding week in 2019. Reductions were pronounced in all age groups but particularly apparent for prescriptions for children aged 0–4 years. These data were compared with weekly prescriptions for a selection of non-respiratory antibiotics and no difference was seen between 2020 and 2019. Conclusions Trends in antibiotic prescription data show that after an initial surge, and following ‘lockdown’ in Scotland, the total number of prescriptions for antibiotics commonly used for respiratory infections fell. We believe this is the first published national evaluation of the impact of COVID-19 on community use of antibiotics. Further analysis of national data is planned to provide a greater understanding of the reasons behind these trends.

We have defined the incidence of CR-BSI in a cohort of patients from a tertiary referral hospital, the rates comparing favourably with those reported for similar populations. We were unable to demonstrate significant differences in any patient or catheter variables between those with and without CR-BSI. The AOLC test used alone was unhelpful as a method to diagnose in situ CVC infection in this patient population.

This article discusses the care of a 62-year-old man with non-small-cell lung cancer and associated cancer anorexia-cachexia syndrome (CACS), and demonstrates common challenges faced by such patients and their family caregivers. The case description illustrates the fragmented approach of various disciplines to the patient's CACS care, resulting in undertreatment, delayed and burdensome visits, and patient and caregiver frustration and emotional distress. The mounting problems that arise for the patient over time exemplify the absence of a shared mental model among the various providers, patient, and caregiver for the care of CACS. Shared knowledge among providers regarding the tasks to be performed, the other clinicians' functions, and optimal processes for CACS care was limited. Each provider was responsive to individual symptoms, rather than conceptualizing the constellation of symptoms as a syndrome that warrants coordinated care among clinicians. This resulted in the patient and the family caregiver being at odds with their various providers instead of working in partnership with a shared understanding toward common goals. Team mental models have the potential to enhance development and implementation of care plans and improve patient care and satisfaction by helping clinical care teams establish team membership, identify shared tasks, and facilitate interactions. To help inform ongoing clinical practice and research, this article demonstrates how clinicians at one cancer center are leveraging a team mental model to form and support an interdisciplinary CACS team that provides coordinated patient-centered care.

BackgroundFor cancer patients with an unplanned hospitalization, estimating survival has been limited. We examined factors predicting survival and investigated the concept of using a deficit‐accumulation survival index (DASI) in this population.MethodsData were abstracted from medical records of 145 patients who had an unplanned 30‐day readmission between 01/01/16 and 09/30/16. Comparison data were obtained for patients who were admitted as close in time to the date of index admission of a study patient, but who did not experience a readmission within 30 days of their discharge date. Our survival analysis compared those readmitted within 30 days versus those who were not. Scores from 23 medical record elements used in our DASI system categorized patients into low‐, moderate‐, and high‐score groups.ResultsThirty‐day readmission was strongly associated with the survival (adjusted hazard ratio [HR] 2.39; 95% confidence interval [CI], 1.46‐3.92). Patients readmitted within 30 days of discharge from index admission had a median survival of 147 days (95% CI, 85‐207) versus patients not readmitted who had not reached median survival by the end of the study (P < .0001). DASI was useful in predicting the survival; median survival time was 78 days (95% CI, 61‐131) for the high score, 318 days (95% CI, 207‐426) for the moderate score, and not reached as of 426 days (95% CI, 251 to undetermined) for the low‐score DASI group (P < .0001).ConclusionsPatients readmitted within 30 days of an unplanned hospitalization are at higher risk of mortality than those not readmitted. A novel DASI developed from clinical documentation may help to predict survival in this population.