Infection control and the prevalence, management and outcomes of SARS-CoV-2 infections in mental health wards in London, UK: lessons learned from wave 1 to wave 2.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) has high morbidity and mortality in older adults and people with dementia. Infection control and prevention measures potentially reduce transmission within hospitals. AIMS: We aimed to replicate our earlier study of London mental health in-patients to examine changes in clinical guidance and practice and associated COVID-19 prevalence and outcomes between COVID-19 waves 1 and 2 (1 March to 30 April 2020 and 14 December 2020 to 15 February 2021). METHOD: We collected the 2 month period prevalence of wave 2 of COVID-19 in older (≥65 years) in-patients and those with dementia, as well as patients' characteristics, management and outcomes, including vaccinations. We compared these results with those of our wave 1 study. RESULTS: Sites reported that routine testing and personal protective equipment were available, and routine patient isolation on admission occurred throughout wave 2. COVID-19 infection occurred in 91/358 (25%; 95% CI 21-30%) v. 131/344, (38%; 95% CI 33-43%) P < 0.001 in wave 1. Hospitals identified more asymptomatic carriers (26/91; 29% v. 16/130; 12%) and fewer deaths (12/91; 13% v. 19/131; 15%; odds ratio = 0.92; 0.37-1.81) compared with wave 1. The patient vaccination uptake rate was 49/58 (85%). CONCLUSIONS: Patients in psychiatric in-patient settings, mostly admitted without known SARS-CoV-2 infection, had a high risk of infection compared with people in the community but lower than that during wave 1. Availability of infection control measures in line with a policy of parity of esteem between mental and physical health appears to have lowered within-hospital COVID-19 infections and deaths. Cautious management of vulnerable patient groups including mental health patients may reduce the future impact of COVID-19

A simple genetic algorithm for calibration of stochastic rock discontinuity networks

Este artículo propone un método para llevar a cabo la calibración de las familias de discontinuidades en macizos rocosos. We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications

Effect of body mass index on depression in a UK cohort of 363 037 obese patients: A longitudinal analysis of transition

With obesity levels increasing, it is important to consider the mental health risks associated with this condition to optimize patient care. Links between depression and obesity have been explored, but few studies focus on the risk profiles of patients across stratified body mass index (BMI) classes above 30 kg/m2 . This study aims to determine the impact of BMI on depression risk in patients with obesity and to investigate trends of depression in a large cohort of British patients with BMI > 30 kg/m2 . A nationwide primary care database, the Clinical Practice Research Datalink (CPRD), was analysed for diagnoses of obesity (BMI > 30 kg/m2 ). Obese patients were then sub-classified into seven BMI categories. Primary health care-based records of patients entered in the CPRD were analysed. A total of 363 037 patients had a BMI ≥ 30 kg/m2 ; of these patients 97 392 (26.8%) also had a diagnosis of depression. Absolute event rates over time and hazard risk of depression were analysed by BMI category. On Cox regression analysis of time to development of depression, the cumulative hazard increased significantly and linearly across BMI groups (P 60 kg/m2 had a 98% higher risk (HR 1.988, CI 1.513-2.612). This study identified the prevalence and time course of depression in a cohort of obese patients in the United Kingdom. Findings suggest the risk of depression is directly proportional to BMI above 30 kg/m2 . Therefore, clinicians should note higher BMI levels confer increased risk of depression