What works to support carers of older people and older carers? an international evidence map of interventions and outcomes.

BACKGROUND: Unpaid carers of older people, and older unpaid carers, experience a range of adverse outcomes. Supporting carers should therefore be a public health priority. Our understanding of what works to support carers could be enhanced if future evaluations prioritise under-researched interventions and outcomes. To support this, we aimed to: map evidence about interventions to support carers, and the outcomes evaluated; and identify key gaps in current evidence. METHODS: Evidence gap map review methods were used. Searches were carried out in three bibliographic databases for quantitative evaluations of carer interventions published in OECD high-income countries between 2013 and 2023. Interventions were eligible if they supported older carers (50 + years) of any aged recipient, or any aged carers of older people (50 + years). FINDINGS: 205 studies reported across 208 publications were included in the evidence map. The majority evaluated the impact of therapeutic and educational interventions on carer burden and carers' mental health. Some studies reported evidence about physical exercise interventions and befriending and peer support for carers, but these considered a limited range of outcomes. Few studies evaluated interventions that focused on delivering financial information and advice, pain management, and physical skills training for carers. Evaluations rarely considered the impact of interventions on carers' physical health, quality of life, and social and financial wellbeing. Very few studies considered whether interventions delivered equitable outcomes. CONCLUSION: Evidence on what works best to support carers is extensive but limited in scope. A disproportionate focus on mental health and burden outcomes neglects other important areas where carers may need support. Given the impact of caring on carers' physical health, financial and social wellbeing, future research could evaluate interventions that aim to support these outcomes. Appraisal of whether interventions deliver equitable outcomes across diverse carer populations is critical.

Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit.

BACKGROUND: Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation. OBJECTIVE: The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation. METHODS: We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts. RESULTS: A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting. CONCLUSIONS: Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.

Closing the loop between horizon scanning and health technology assessment - an overview of topics submitted for appraisal in England.

OBJECTIVES: Horizon scanning for health technology appraisal (HTA) in England involves topic notification to the National Institute for Health and Care Excellence (NICE) via technology briefings. This activity is undertaken by the Innovation Observatory with submission timelines designed to ensure that HTA decisions align with regulatory approval time. In this paper, we aimed to track and assess the progression and current status of the topics notified for HTA and provide a descriptive analysis of these topics. METHODS: Technology briefings were mapped from submission to NICE technology appraisal/highly specialized technologies recommendations from April 2017 until October 2021. This was done using a combination of searches on Google and NICE website, searching a downloadable spreadsheet containing NICE topic selection decisions, and querying NICE Topic Selection team. Analysis was undertaken regarding type of indications and interventions of submitted topics and published guidance. RESULTS: Six-hundred and ninety-three topics entered the NICE scoping process, of which 94 percent were prioritized. As of November 2021, approximately 39 percent of prioritized topics were in scoping/in progress, 31 percent were proposed/completed, 20 percent were suspended/terminated, and 4 percent were referred back to Innovation Observatory (IO) for further monitoring. CONCLUSIONS: Our work demonstrates that horizon scanning for HTA is a complex and time-intensive process. Timelines and progress through HTA is challenging due to the growing number of innovative medicines, significant uncertainties, and limited transparency in clinical development and regulatory pathways. A better understanding of clinical trials and regulatory requirements may help eliminate some of this uncertainty and improve timely HTA.

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.