Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach.

BACKGROUND: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. OBJECTIVE: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. METHODS: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder-related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post's language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. RESULTS: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. CONCLUSIONS: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

Special Report from the CDC: Evaluating the dissemination of CDC's MyMobility Plan: Findings and lessons learned.

INTRODUCTION: Age-related changes (e.g., cognitive, physiologic) can affect an individual's mobility and increase risks for falls and motor-vehicle crashes, which are leading causes of injuries and injury deaths among older Americans. To address this issue, CDC developed MyMobility Plan (MMP) products to help older adults make plans to reduce injury risks and promote safe mobility. In 2019, MMP products were disseminated to older adults and partner organizations. Dissemination strategies consisted of digital and print distribution and partner outreach. METHODS: To assess dissemination efforts, a process (or implementation) evaluation was conducted from January to June 2019. Data were collected for 17 indicators (e.g., counts of webpage visits, product downloads, social media posts). Key informant interviews were conducted with partners, and qualitative analyses of interview data were undertaken to identify key themes related to their dissemination experiences. RESULTS: Findings showed the dissemination resulted in 13,425 product downloads and print copy orders and reached almost 155,000 individuals through email subscriber lists, websites, webinars, and presentations. It is unknown what proportion of these individuals were older adults. Social media metrics were higher than expected, and 58 partners promoted products within their networks. Partner interviews emphasized the need for guidance on dissemination, collaboration with local partners, and integration of the products within a program model to ensure broader reach to and use by older adults. CONCLUSIONS: The evaluation of the dissemination campaign identified strategies that were successful in creating exposure to the MMP and others that could improve reach in the future. Those strategies include meaningful and early partner engagement for dissemination. PRACTICAL APPLICATIONS: Building in evaluation from the start can facilitate development of appropriate data collection measures to assess project success. Engaging partners as active disseminators in the planning stages can help increase the reach of public health tools and resources.