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Objectives: To describe vaccine and booster uptake by neighborhood-level factors in California. Methods: We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions. Results: In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region. Conclusions: Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.
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In the absence of advice from the workplace regulator, a model respiratory protection programme for healthcare workers is presented based in healthcare and wider industry experience. Hospital and other healthcare institutions can use this as a basis for their programmes in preparation for the next infective disease outbreak. Copyright © NZMA.
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BACKGROUND: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. OBJECTIVE: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. METHODS: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. RESULTS: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. CONCLUSIONS: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. TRIAL REGISTRATION: ClinicalTrials.gov NCT03538860; https://clinicaltrials.gov/ct2/show/NCT03538860.
ABSTRACT
Endoscopy is an essential component of gastroenterology, allowing for the diagnosis and management of a variety of gastrointestinal diseases. Although most endoscopies are considered to be low risk procedures, several factors including the sedation, patient, and procedure play a role in determining overall risk. Patient assessment prior to endoscopy is essential to risk-stratification and provides an opportunity to review comorbidities, adjust medications if necessary, and identify an optimal sedation plan. Several best practice recommendations and guidelines have been developed to ensure that safe, high-quality endoscopies are performed to minimize risks and optimize outcomes. The purpose of this review is to highlight best practices related to pre-endoscopic evaluation and, when available, review quality indicators.