Mental health impacts of COVID-19 in Ireland and the need for a secondary care mental health service response.

The COVID-19 pandemic is a global health emergency, the scale, speed and nature of which is beyond anything most of us have experienced in our lifetimes. The mental health burden associated with this pandemic is also likely to surpass anything we have previously experienced. In this editorial, we seek to anticipate the nature of this additional mental health burden and make recommendations on how to mitigate against and prepare for this significant increase in mental health service demand.

A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK.

Objective: Given the uncertainty about the trends and extent of the rapidly evolving COVID-19 outbreak, and the lack of extensive testing in the United Kingdom, our understanding of COVID-19 transmission is limited. We proposed to use Twitter to identify personal reports of COVID-19 to assess whether this data can help inform as a source of data to help us understand and model the transmission and trajectory of COVID-19. Methods: We used natural language processing and machine learning framework. We collected tweets (excluding retweets) from the Twitter Streaming API that indicate that the user or a member of the user's household had been exposed to COVID-19. The tweets were required to be geo-tagged or have profile location metadata in the UK. Results: We identified a high level of agreement between personal reports from Twitter and lab-confirmed cases by geographical region in the UK. Temporal analysis indicated that personal reports from Twitter appear up to 2 weeks before UK government lab-confirmed cases are recorded. Conclusions: Analysis of tweets may indicate trends in COVID-19 in the UK and provide signals of geographical locations where resources may need to be targeted or where regional policies may need to be put in place to further limit the spread of COVID-19. It may also help inform policy makers of the restrictions in lockdown that are most effective or ineffective.

Toward Using Twitter Data to Monitor COVID-19 Vaccine Safety in Pregnancy: Proof-of-Concept Study of Cohort Identification.

BACKGROUND: COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. OBJECTIVE: The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. METHODS: We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. RESULTS: We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies. CONCLUSIONS: Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies.

Keyworker mediated enhancement of physical health in patients with first episode psychosis: A feasibility/acceptability study.

AIM: Early intervention for people experiencing first episode psychosis is a priority, and keyworkers are vital to such services. However, keyworkers' roles in addressing first episode psychosis patients' physical health are under researched. This study addresses this knowledge gap by evaluating a keyworker-mediated intervention promoting physical health among first episode psychosis patients. METHODS: The study was informed by the Medical Research Council's Framework for Complex Interventions to Improve Health. First episode psychosis participants were recruited from three Irish mental health services. The intervention was evaluated in terms of its feasibility/acceptability. RESULTS: Feasibility outcomes were mixed (recruitment rate = 24/68 [35.3%]; retention rate = 18/24 [75%]). The baseline sample was predominantly male (M:F ratio = 13:6; Med age = 25 y; IQR = 23-42 y). Common health issues among participants included overweightness/obesity (n = 11) and substance use (smoking/alcohol consumption [n = 19]). Participants' initial health priorities included exercising more (n = 10), improving diet (n = 6), weight loss (n = 7) and using various health/healthcare services. The intervention's acceptability was evidenced by the appreciation participants had for physical health keyworkers' support, as well as the healthy lifestyle, which the intervention promoted. Acceptability was somewhat compromised by a low-recruitment rate, variable linkages between keyworkers and general practitioners (GPs) and COVID-19 restrictions. CONCLUSIONS: Physical health-oriented keyworker interventions for first episode psychosis patients show promise and further evaluation of such initiatives is warranted. Future interventions should be mindful of participant recruitment challenges, strategies to enhance relationships between keyworkers and GPs, and if necessary, they should mitigate COVID-19 restrictions' impacts on care.

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.

BACKGROUND: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. OBJECTIVE: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. METHODS: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out "reported speech" (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. RESULTS: Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state-level geolocations. CONCLUSIONS: We have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state-level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.