Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of "high in the east and low in the west." Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360794 | PMC |
http://dx.doi.org/10.3389/fpubh.2022.949482 | DOI Listing |
Australas Psychiatry
January 2025
College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia; Consortium of Australian-Academic Psychiatrists for Independent Policy Research and Analysis, Canberra, ACT, Australia; Department of Psychiatry, Monash University, Clayton, VIC, Australia.
Objective: Attention-deficit hyperactivity disorder (ADHD) medication prescriptions in Australia have grown sharply in recent years. We examined the association between online interest in ADHD and prescriptions.
Methods: Monthly Pharmaceutical Benefits Scheme (PBS) and Repatriation PBS (RPBS) Item Reports of ADHD prescriptions and Australian ADHD-related Google Trends (GT) data (2004-2023) were sourced.
PLoS One
January 2025
Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
This study examines whether the detrimental effects of the COVID-19 pandemic on the affectivity of the population extend one year after the outbreak. In an online-mobile session, participants completed surveys (i.e.
View Article and Find Full Text PDFFront Psychiatry
January 2025
Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Introduction: Unipolar and bipolar mood disorders in older adults are accompanied by cognitive impairment, including executive dysfunction, with a severe impact on daily life. Up and till now, strategies to improve cognitive functioning in late-life mood disorders (LLMD) are sparse. Therefore, we aimed to assess the efficacy of adaptive, computerized cognitive training (CT) on executive and subjective cognitive functioning in LLMD.
View Article and Find Full Text PDFIntern Emerg Med
January 2025
The Toxikon Consortium, 1950 West Polk St, 7th Floor, Chicago, IL, 60612, USA.
Carbon monoxide (CO) poisoning continues to result in hospitalization and mortality. We sought to analyze risk factors associated with inpatient admission for CO poisoning. Retrospective study of the US National Inpatient Sample (NIS) database.
View Article and Find Full Text PDFBioinformatics
January 2025
Geneis Beijing Co., Ltd, Beijing 100102, China.
Motivation: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple Instance Learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!