ABSTRACT
Purpose: We partnered with a local Federally Qualified Health Center (FQHC) to test implementation of evidence-based interventions (EBI) promoting Fecal Immunochemical Test (FIT) CRC screening in an environment in which colonoscopy has been the prevailing screening strategy. We report on implementation adaptations and preliminary results. Background: Sociocultural and medical concerns are barriers to colonoscopy uptake in some populations. An additional barrier to CRC screening is system level capacity for colonoscopy that results in a back log of cases and long wait times. With Covid-19, the additional backlog in overdue CRC screening has underscored the need to expand FIT testing capacity to address screening needs and to pre-empt further racial/ethnic and SES disparities in CRC outcomes. This trial tests the unique and additive value of multiple EBIs for increasing CRC screening (primarily through FIT testing, but also colonoscopy when indicated) while evaluating the success of implementing these approaches. EBIs include the use of medical reminders, addressing the structural barriers (social determinants of health [SDOH]), and assistance from community health workers. Methods: Participants (3500), ages 45-75, were identified from a large FQHC in New Haven, CT and determined to be overdue for CRC screening. Participants were randomly assigned to one of the four arms of the study: 1) Provider reminder (overdue for CRC screening) only;2) Provider Reminder + SDOH short message and one-size-fits all link to resources;3) Provider Reminder + SDOH short message and offer for individualized navigation (trained navigators from local community) to address SDOH and other barriers;4) Provider Reminder + offer to participate in a CRC educational program as phase 2 of the NCI's Screen to Save program (not an EBI). Preliminary data on uptake of CRC screening will be presented. Results: With input from stakeholders, we have: 1) lowered age eligibility from 50 to 45 to align with new guidelines;2) expanded the target population to 2 additional satellite clinics, more than doubling the proposed study enrollment;3) incorporated design changes in the patient reminders. The collaboration between research team and clinician stakeholders has been critical in minimizing disruptions to clinical workflow while assuring fidelity to the evidence-based interventions. Preliminary outcomes (within one month of intervention) on uptake of intervention across the 4 arms of the study, i.e., referral for CRC screening and test completion will be presented. Conclusion: The unique challenges of this urban community of primarily African American/Black, Hispanic/Latinx and/or low socioeconomic status individuals stem from the disproportionate burden of SDOH barriers. Findings will inform primary care setting implementation of EBIs to address the anticipated increase in disparities in CRC screening, exacerbated by COVID-19 changes in health care access and utilization, as well as the increased demand associated with the change in guidelines.
ABSTRACT
Masks are believed to slow the spread of Covid-19, and can prevent many deaths, yet this inexpensive, common sense public health measure has ignited a fierce debate in the U.S. Opponents of masks or anti-maskers have resorted to measures such as organizing protests and marches to make their views public. They have also taken to social media platforms to vigorously argue against the use of masks, and spread misinformation, lies, and myths regarding their use. Even with the advent of vaccines, masks are still likely to be recommended for a long time. It is therefore necessary to identify those tweets that spread falsehoods regarding the use and effectiveness of masks in order to limit their appeal and damage. This paper proposes a classification framework to detect anti-mask tweets from social media dialogue shared on Twitter during the months of July and August 2020. The framework relies on popular machine learning models trained using a combination of linguistic, auxiliary, psycholinguistic and sentiment features for detection. The proposed classification framework can detect anti-mask tweets with excellent accuracy of over 90%, and hence, it can be used to tag tweets that sow misinformation about masks before they spread through the ether and influence people. © 2021 IEEE.