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Background: Racial inequities in life expectancy, driven by structural racism, have been documented at the state and county levels; however, less information is available at the city level where local policy change generally happens. Furthermore, an assessment of life expectancy during the decade preceding COVID-19 provides a point of comparison for life expectancy estimates and trends post COVID-19 as cities recover., Methods: Using National Vital Statistics System mortality data and American Community Survey population estimates, we calculated the average annual city-level life expectancies for the non-Hispanic Black (Black), non-Hispanic White (White), and total populations. We then calculated the absolute difference between the Black and White life expectancies for each of the 30 cities and the U.S. We analyzed trends over four time periods (2008-2010, 2011-2013, 2014-2016, and 2017-2019)., Results: In 2017-2019, life expectancies ranged from 72.75 years in Detroit to 83.15 years in San Francisco (compared to 78.29 years for the U.S.). Black life expectancy ranged from 69.94 years in Houston to 79.04 years in New York, while White life expectancy ranged from 75.18 years in Jacksonville to 86.42 years in Washington, DC. Between 2008-2010 and 2017-2019, 17 of the biggest cities experienced a statistically significant improvement in life expectancy, while 9 cities experienced a significant decrease. Black life expectancy increased significantly in 14 cities and the U.S. but decreased significantly in 4 cities. White life expectancy increased significantly in 17 cities and the U.S. but decreased in 8 cities. In 2017-2019, the U.S. and all but one of the big cities had a significantly longer life expectancy for the White population compared to the Black population. There was more than a 13-year difference between Black and White life expectancies in Washington, DC (compared to 4.18 years at the national level). From 2008-2010 to 2017-2019, the racial gap decreased significantly for the U.S. and eight cities, while it increased in seven cities., Conclusion: Urban stakeholders and equity advocates need data on mortality inequities that are aligned with city jurisdictions to help guide the allocation of resources and implementation of interventions., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: The commercial application Openfit allows for automatic identification and quantification of food intake through short video capture without a physical reference marker. There are no known peer-reviewed publications on the validity of this Nutrition Artificial Intelligence (AI)., Objectives: To test the validity of Openfit to identify food automatically and semiautomatically (with user correction), test the validity of Openfit at quantifying energy intake (kcal) automatically and semiautomatically, and assess satisfaction and usability of Openfit., Methods: During a laboratory-based visit, adults (7 male and 17 female), used Openfit to automatically and semiautomatically record provided meals, which were covertly weighed. Foods logged were identified as an "exact match," "far match," or an "intrusion" using Food and Nutrient Database for Dietary Studies (FNDDS) codes. Descriptive data were stratified by meal, food item, and FNDDS group, and presented with or without beverages. Bland-Altman analyses assessed errors over levels of energy intake. Participants completed a User Satisfaction Survey (USS) and the Computer Systems Usability Questionnaire (CSUQ). Open-ended questions were assessed with qualitative methods., Results: Exact matches, far matches, and intrusions were 46%, 41%, and 13% for automated identification, and 87%, 23%, and 0% for semiautomated identification, respectively. Error for automated and semiautomated energy estimates were 43% and 33% with beverages, and 16% and 42% without beverages. Bland-Altman analyses indicated larger error for higher energy meals. Overall mean scores were 2.4 for the CSUQ and subscale means scores ranged from 4.1 to 5.5. for the USS. Participants recommended improvements to Openfit's Nutrition AI, manual estimation, and overall app., Conclusion: Openfit worked relatively well for automatically and semiautomatically identifying foods. Error in automated energy estimates was relatively high; however, after excluding beverages, error was relatively low (16%). For semiautomated energy estimates, error was comparable to previous studies. Improvements to the Nutrition AI, manual estimation and overall application may increase Openfit's usability and validity.This trial was registered at clinicaltrials.gov as NCT05343585., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Introduction: As the COVID-19 pandemic placed a spotlight on the health inequities in the United States, this study aimed to determine the local programmatic needs of community organizations (CO) delivering COVID-19 interventions across Chicago., Methods: In the summer of 2021, the Chicagoland CEAL Program interviewed 34 COs that were providing education, testing, and/or vaccinations in communities experiencing poor COVID-19 outcomes. The interviews were analyzed thematically and organized around logistical challenges and funding/resource needs., Results: The COs routinely offered testing (50%) or vaccinations (74%), with most (56%) employing some programmatic evaluation. Programs utilizing trusted-messenger systems were deemed most effective, but resource-intensive. CO specific needs clustered around sustaining effective outreach strategies, better CO coordination, wanting comprehensive trainings, improving program evaluation, and promoting services and programs., Conclusion: The COs reached populations with low-vaccine confidence using trusted messengers to overcome mistrust. However, replenishment of the resources needed to sustain such strategies should be prioritized. Leveraging the Chicagoland CEAL Program to help negotiate community organizations' interorganizational coordination, create training programs, and provide evaluation expertise are deliverable supports that may bolster COVID-19 prevention., Policy Implications: Achieving health justice requires that all institutions of power participate in meaningful community engagement, help build community capacity, and infuse health equity throughout all aspects of the research and program evaluation processes., Competing Interests: AMM, SL, LL, and PT were employed by Equal Hope. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Moskowitz, Silva, Castañeda, Battalio, Hartstein, Murphy, Ndebele, Switalski, Lomahan, Lacson, Plum, Canty, Sandoval, Thomas, De Pablo, Spring and Martin.)

Background: Atrial Fibrillation (AF) is the most common tachyarrhythmia and is associated with increased risk of stroke, morbidity and mortality. AF is responsible for up to a quarter of all strokes and is often asymptomatic until a stroke occurs.Screening for AF is a valuable approach to reduce the burden of stroke in the population., Objectives: The motivation for this review was to synthesise and appraise the evidence for screening for AF in the community. The aims of this scoping review are 1). To describe the prevalence of newly diagnosed AF in screening programmes 2). Identify which techniques/ tools are employed for AF screening 3). To describe the setting and personnel involved in screening for AF., Eligibility Criteria: All forms of AF screening in adults (≥18 years) in primary and community care settings., Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping reviews (PRISMA-ScR)., Results: Fifty-nine papers were included; most were cross-sectional studies (n=41) and RCTs (n=7). Prevalence of AF ranged from 0-34.5%. Screening tools and techniquesincluded the 12-lead ECG (n=33), the 1-lead ECG smartphone based Alivecor® (n=14) and pulse palpation (n=12). Studies were undertaken in community settings (n=30) or in urban/rural primary care (n=28). Personnel collecting research data were in the main members of the research team (n=31), GPs (n=16), practice nurses (n=10), participants (n=8) and pharmacists (n=4)., Conclusion: Prevalence of AF increased with advancing age. AF screening should target individuals at greatest risk of the condition including older adults≥65 years of age. Emerging novel technologies may increase the accessibility of AF screening in community and home settings. There is a need for high quality research to investigate AF prevalence and establish accuracy and validity for traditional versus novel screening tools used to screen for AF.