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Scand J Caring Sci ; 2022 May 27.
Article in English | MEDLINE | ID: covidwho-1874460


Most perinatal research relating to COVID-19 focuses on its negative impact on maternal and parental mental health. Currently, there are limited data on how to optimise positive health during the pandemic. We aimed to bridge this knowledge gap by exploring how women have adapted to becoming a new parent during the pandemic and to identify elements of resilience and growth within their narratives. Mothers of infants under the age of 4 months were recruited as part of a wider UK mixed-methods study. Semi-structured interviews with 20 mothers elicited data about how COVID-19 had influenced their transition to parent a new infant, and if and how they adapted during the pandemic, what strategies they used, and if and how these had been effective. Directed qualitative content analysis was undertaken, and pre-existing theoretical frameworks of resilience and post-traumatic growth (PTG) were used to analyse and interpret the data set. The findings show evidence of a range of resilience and PTG concepts experienced during the pandemic in this cohort. Salient resilience themes included personal (active coping, reflective functioning, and meaning-making), relational (social support, partner relationships, and family relationships), and contextual (health and social connectedness) factors. There was also evidence of PTG in terms of the potential for new work-related and leisure opportunities, and women developing wider and more meaningful connections with others. Although further research is needed, and with individuals from diverse socioeconomic backgrounds, these findings emphasise the significance of social support and connectivity as vital to positive mental health. Opportunities to increase digital innovations to connect and support new parents should be maximised to buffer the negative impacts of further social distancing and crisis situations.

Eur Radiol ; 32(7): 4446-4456, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1707890


OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

COVID-19 , Deep Learning , Humans , Intensive Care Units , Radiography , X-Rays
PLoS Comput Biol ; 17(7): e1009146, 2021 07.
Article in English | MEDLINE | ID: covidwho-1305573


SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

COVID-19/prevention & control , Contact Tracing , Systems Analysis , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , COVID-19 Testing , COVID-19 Vaccines/administration & dosage , Disease Outbreaks , Humans , Physical Distancing , Quarantine , SARS-CoV-2/isolation & purification