Do Longitudinal Trends in Tobacco 21-Related Media Coverage Correlate with Policy Support? an Exploratory Analysis Using Supervised and Unsupervised Machine Learning Methods

The application of artificial intelligence in health policy: a scoping review

BACKGROUND

Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.

METHODS

We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.

RESULTS

The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.

CONCLUSION

Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.

Scanned information exposure and support for tobacco regulations among US youth and young adult tobacco product users and non-users

The influences of information exposure on youth and young adults' (YYA) support for smoking/vaping regulations have been understudied. This study examines (i) the relationships between routine exposure to (i.e. scanning) anti-smoking/pro-vaping information and YYA support for anti-smoking/vaping regulations and (ii) whether these relationships differ across YYA users and non-users of tobacco products. We analyzed the data from a nationally representative two-wave rolling cross-sectional survey of YYA in the United States, collected from 2014 to 2017 (baseline n = 10 642; follow-up n = 4001). Less than 5% of the participants ever scanned pro-smoking and anti-vaping information. Scanning anti-smoking information had significant positive relationships with support for all anti-smoking policies cross-sectionally, and this pattern was longitudinally significant in two anti-smoking policy contexts. Scanning pro-vaping information had significant negative associations with support for anti-vaping policies cross-sectionally, but not longitudinally. The lagged positive relationships between scanning anti-smoking information and support for anti-smoking regulations were stronger among YYA smokers than among YYA non-smokers, whereas evidence from adult data suggested the opposite pattern. The findings suggest that scanning information can affect YYA support for tobacco regulations. Future efforts are required to investigate mechanisms underlying the influences of scanned information on YYA support for tobacco regulations.

Machine learning applications in tobacco research: a scoping review

OBJECTIVE

Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis.

DATA SOURCES

MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded.

STUDY SELECTION

Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis.

DATA EXTRACTION

Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted.

DATA SYNTHESIS

74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed.

CONCLUSIONS

ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.