Artificial Intelligence-Supported Development of Health Guideline Questions.

BACKGROUND: Guideline questions are typically proposed by experts. OBJECTIVE: To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned. DESIGN: Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician. SETTING: Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines. PARTICIPANTS: None. MEASUREMENTS: Frequency of relevant questions generated. RESULTS: The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned. LIMITATION: Single case study (ARIA guidelines). CONCLUSION: Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels. PRIMARY FUNDING SOURCE: Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.

English tweets on allergy: Content analysis and association with surveillance data.

BACKGROUND: Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data. METHODS: We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition. RESULTS: We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment - the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from -0.004 ("asthma") to -0.083 ("allergic rhinitis"). CONCLUSIONS: Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma."

The inclusion of health in major global reports on climate change and biodiversity.

This article argues that human health has become a key consideration in recent global reports on climate change and biodiversity produced by various international organisations; however, greater attention must be given to the unequal health impacts of climate change and biodiversity loss around the world and the different health adaptation measures that are urgently required.

Tracking progress on health and climate change in Europe.

Left unabated, climate change will have catastrophic effects on the health of present and future generations. Such effects are already seen in Europe, through more frequent and severe extreme weather events, alterations to water and food systems, and changes in the environmental suitability for infectious diseases. As one of the largest current and historical contributors to greenhouse gases and the largest provider of financing for climate change mitigation and adaptation, Europe's response is crucial, for both human health and the planet. To ensure that health and wellbeing are protected in this response it is essential to build the capacity to understand, monitor, and quantify health impacts of climate change and the health co-benefits of accelerated action. Responding to this need, the Lancet Countdown in Europe is established as a transdisciplinary research collaboration for monitoring progress on health and climate change in Europe. With the wealth of data and academic expertise available in Europe, the collaboration will develop region-specific indicators to address the main challenges and opportunities of Europe's response to climate change for health. The indicators produced by the collaboration will provide information to health and climate policy decision making, and will also contribute to the European Observatory on Climate and Health.