Public Health Response to COVID-19 Cases in Correctional and Detention Facilities - Louisiana, March-April 2020.

Correctional and detention facilities face unique challenges in the control of infectious diseases, including coronavirus disease 2019 (COVID-19) (1-3). Among >10 million annual admissions to U.S. jails, approximately 55% of detainees are released back into their communities each week (4); in addition, staff members at correctional and detention facilities are members of their local communities. Thus, high rates of COVID-19 in correctional and detention facilities also have the potential to influence broader community transmission. In March 2020, the Louisiana Department of Health (LDH) began implementing surveillance for COVID-19 among correctional and detention facilities in Louisiana and identified cases and outbreaks in many facilities. In response, LDH and CDC developed and deployed the COVID-19 Management Assessment and Response (CMAR) tool to guide technical assistance focused on infection prevention and control policies and case management with correctional and detention facilities. This report describes COVID-19 prevalence in correctional and detention facilities detected through surveillance and findings of the CMAR assessment. During March 25-April 22, 489 laboratory-confirmed COVID-19 cases, including 37 (7.6%) hospitalizations and 10 (2.0%) deaths among incarcerated or detained persons, and 253 cases, including 19 (7.5%) hospitalizations and four (1.6%) deaths among staff members were reported. During April 8-22, CMAR telephone-based assessments were conducted with 13 of 31 (42%) facilities with laboratory-confirmed cases and 11 of 113 (10%) facilities without known cases. Administrators had awareness and overall understanding of CDC guidance for prevention of transmission in these facilities but reported challenges in implementation, related to limited space to quarantine close contacts of COVID-19 patients and inability of incarcerated and detained persons to engage in social distancing, particularly in dormitory-style housing. CMAR was a useful tool that helped state and federal public health officials assist multiple correctional and detention facilities to better manage COVID-19 patients and guide control activities to prevent or mitigate transmission.

Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science.

Background: Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals' preventive behaviors, including masking, testing, and vaccine uptake. Objective: In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation. Methods: We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a novel methodological framework, comprising qualitative inquiry, computational methods, and quantitative network models to analyze publicly available social media data sets to model content-specific misinformation dynamics and guide content tailoring efforts. As part of community needs assessment, we conducted 11 semistructured interviews, 4 listening sessions, and 3 focus groups with community scientists. Further, we used our data repository with 416,927 COVID-19 social media posts to gather information diffusion patterns through digital channels. Results: Our results from community needs assessment revealed the complex intertwining of personal, cultural, and social influences of misinformation on individual behaviors and engagement. Our social media interventions resulted in limited community engagement and indicated the need for consumer advocacy and influencer recruitment. The linking of theoretical constructs underlying health behaviors to COVID-19-related social media interactions through semantic and syntactic features using our computational models has revealed frequent interaction typologies in factual and misleading COVID-19 posts and indicated significant differences in network metrics such as degree. The performance of our deep learning classifiers was reasonable, with an F-measure of 0.80 for speech acts and 0.81 for behavior constructs. Conclusions: Our study highlights the strengths of community-based field studies and emphasizes the utility of large-scale social media data sets in enabling rapid intervention tailoring to adapt grassroots community interventions to thwart misinformation seeding and spread among minority communities. Implications for consumer advocacy, data governance, and industry incentives are discussed for the sustainable role of social media solutions in public health.