Background: Our study aimed to describe the variation in the frequency of correct mask use among pedestrians in the first and second waves of the COVID-19 pandemic in high-flow indoor public spaces from different geographic and social settings in Peru.
Methods: We carried out a cross-sectional exploratory study among pedestrians in Lima (the capital city) and other coastal and highland cities in Peru. Pedestrians were directly observed by trained medical students in 2 high-flow indoor areas at different times in November 2020 (first wave) and October 2021 (second wave). Primary outcomes included the frequencies of mask use and correct use. We applied multinomial logistic models and estimated crude and adjusted relative prevalence ratios for sex, age, obesity, and location. Additionally, we used binomial generalized linear models to estimate prevalence ratios in crude and adjusted models.
Results: We included 1996 participants. The frequency of mask use was similar in both years: 96.9% in 2020 and 95.5% in 2021. However, the frequency of correct mask use significantly decreased from 81.9% (95% CI, 79.4-84.3) in 2020 to 60.3% (95% CI, 57.2-67.3) in 2021. In 2020, we observed an increase in the probability of misuse in the cities of Lima (aRP: 1.42; = .021) and Chiclayo (aPR: 1.62, = .001), whereas, in 2021, we noted an increase in the probability of misuse in the cities of Lima (aRP: 1.72; < .001) and Piura (aPR: 1.44; < .001).
Conclusions: The correct mask use decreased during the second wave, although no significant overall variations were observed in mask use in pedestrians between both periods. Also, we found regional differences in correct mask use in both periods.
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http://dx.doi.org/10.1177/21501319221134851 | DOI Listing |
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Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
Introduction: The U.S. Food and Drug Administration's (FDA) pursuit of a low nicotine standard for cigarettes raises concerns that a focus on cigarettes may encourage people to use other combusted tobacco products, undermining the policy's effectiveness.
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Department of Epidemiology and Biostatistics, Indiana University, Bloomington, Indiana, US.
Wearable devices enable the continuous monitoring of physical activity (PA) but generate complex functional data with poorly characterized errors. Most work on functional data views the data as smooth, latent curves obtained at discrete time intervals with some random noise with mean zero and constant variance. Viewing this noise as homoscedastic and independent ignores potential serial correlations.
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Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, United Kingdom.
Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions.
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