Relatively few studies have longitudinally investigated how COVID-19 has disrupted the lives and health of youth beyond the first year of the pandemic. This may be because longitudinal researchers face complex challenges in figuring out how to code time, account for changes in COVID-19 spread, and model longitudinal COVID-19-related trajectories across environmental contexts. This manuscript considers each of these three methodological issues by modeling trajectories of COVID-19 disruption in 1080 youth from 12 cultural groups in nine nations between March 2020-July 2022 using multilevel modeling. Our findings suggest that for studies that attempt to examine cross-cultural longitudinal trajectories during COVID-19, starting such trajectories on March 11, 2020, measuring disruption along 6-month time intervals, capturing COVID-19 spread using death rates and the COVID-19 Health and Containment Index scores, and using modeling methods that combine etic and emic approaches are each especially useful. In offering these suggestions, we hope to start methodological dialogues among longitudinal researchers that ultimately result in the proliferation of research on the longitudinal impacts of COVID-19 that the world so badly needs.
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http://dx.doi.org/10.1007/s11121-024-01726-2 | DOI Listing |
Background: It is increasingly recognized that policies played a role in mitigating or exacerbating health inequities during the COVID-19 pandemic. While US counties were particularly active in policymaking, limited work has characterized geographic and temporal variation in pandemic-era policymaking at the local level, a prerequisite for later studies examining the health effects of these policies. This paper fills this gap by characterizing county-level COVID-19-related policy trajectories over time using a novel national policy database and innovative methods.
View Article and Find Full Text PDFJ Family Med Prim Care
December 2024
Department of Community Medicine, Mahatma Gandhi Medical College, Jaipur, Rajasthan, India.
Background: India shares 2/3 of global TB burden. MDR and HIV coinfections are the main obstacle in achieving the successful TB control because it decrease the therapy effect.
Objective: To analyze the long-term trends of incidence of tuberculosis cases and identify any differences between actual and projected cases after the COVID-19 pandemic.
J Am Med Dir Assoc
January 2025
Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Objectives: This study aimed to evaluate the utility of electronic health record (EHR) diagnosis codes for monitoring SARS-CoV-2 infections among nursing home residents.
Design: A retrospective cohort study design was used to analyze data collected from nursing homes operating under the tradename Signature Healthcare between January 2022 and June 2023.
Setting And Participants: Data from 31,136 nursing home residents across 76 facilities in Kentucky, Tennessee, Indiana, Ohio, North Carolina, Georgia, Alabama, and Virginia were included.
J Med Internet Res
January 2025
Department of Pediatrics, Medical School, University of Michigan, Ann Arbor, MI, United States.
Background: The mental health crisis among college students intensified amid the COVID-19 pandemic, suggesting an urgent need for innovative solutions to support them. Previous efforts to address mental health concerns have been constrained, often due to the underuse or shortage of services. Mobile health (mHealth) technology holds significant potential for providing resilience-building support and enhancing access to mental health care.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Department of Stomatology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China.
Purpose: To perform risk assessment and analysis of potential infection during stomatology workflow in a hospital in the context of a major infectious disease outbreak, and to determine the key failure modes and measures to prevent and control infection.
Method: Following the Failure Modes and Effects Analysis (FMEA) method based on the stomatology workflow, the opinions of 30 domain-experts in related fields were collected through questionnaires to determine all potential failure modes in the severity (S), occurrence (O), and detectability (D) dimensions. The group score was then integrated through the median method and the risk priority number (RPN) was obtained.
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