A rapid priority setting exercise combining existing, emergent evidence with stakeholder knowledge identified broad topic uncertainties.

OBJECTIVE: The project aimed to rapidly identify priority topic uncertainties as a first step to identify future systematic review questions of pertinence to key international faecal incontinence (FI) stakeholders (patients, carers, healthcare professionals, policy makers and voluntary, community or social enterprise representatives). The paper aim is to share our methods, experience and learning with other groups planning to deliver a rapid priority setting exercise. STUDY DESIGN: An evidence gap map incorporated three evidence streams: emerging evidence identified through horizon scanning; existing evidence identified through systematic searches of bibliographic databases; and FI stakeholder insights collected through an international survey. The evidence gap map was presented during an online workshop with stakeholders, where they shared their expertise to expand, refine and rank topic uncertainties using ideation techniques, focus group discussions, consensus techniques and online polling. RESULTS: The multi-step methods used to deliver this priority-setting exercise resulted in identification of broad priority topic uncertainties. The methods appear to have high acceptability and engagement with participants but await full evaluation. CONCLUSIONS: This project successfully followed robust methodology, building upon frameworks from published priority setting and evidence gap mapping projects whilst incorporating strong patient and public involvement components.

Impacts of health care service changes implemented due to COVID-19 on children and young people with long-term disability: A mapping review.

AIM: To identify the research on childhood disability service adaptations and their impact on children and young people with long-term disability during the COVID-19 pandemic. METHOD: A mapping review was undertaken. We searched the World Health Organization Global COVID-19 database using the search terms 'children', 'chronic/disabling conditions', and 'services/therapies'. Eligible papers reported service changes for children (0-19 years) with long-term disability in any geographical or clinical setting between 1st January 2020 and 26th January 2022. Papers were charted across the effective practice and organization of care taxonomy of health system interventions and were narratively synthesized; an interactive map was produced. RESULTS: Reduction of face-to-face care and usual provision had a huge impact on children and families. Adoption of telehealth provided continuity for the care and management of some conditions. There was limited evidence of changes to mental health services, transitions of care, social care, or child-reported satisfaction or acceptability of service changes. INTERPRETATION: The long-term impacts of service change during the pandemic need full evaluation. However, widespread disruption seems to have had a profound impact on child and carer health and well-being. Service recovery needs to be specific to the individual needs of children with a disability and their families. This should be done through coproduction to ensure that service changes meet needs and are accessible and equitable.

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.

Background: There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective: The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods: A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter's advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results: We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people's mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions: Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people's mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.