ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected socially disadvantaged groups;however, the association between socioeconomic status and healthcare utilization among COVID-19 patients remains unclear. Therefore, a systematic review and meta-analysis was conducted to assess the association between socioeconomic status and hospitalization and intensive care unit admission among COVID-19 patients.Methods: PubMed, Embase, and the Cochrane Register of Controlled Trials were searched for relevant litera-ture (updated to Jun 2022). Studies that investigated the association of social deprivation with hospitalization and intensive care unit admission in COVID-19 patients were included. The primary outcomes included risk of hospitalization and intensive care unit admission, measured by odds ratio.Results: Eleven studies covering 2,423,095 patients were included in the meta-analysis. Socially disadvantaged patients had higher odds of hospitalization in comparison to socially advantaged patients (odds ratio 1.25, 95% confidence interval: 1.14 to 1.38;P<0.01). The odds of intensive care unit admission among more deprived patients was not significantly different from that of less deprived patients (odds ratio 1.03, 95% confidence interval: 0.78 to 1.35;P=0.85). These findings were proven robust through subgroup and sensitivity analyses.Conclusion: Socially disadvantaged populations have higher odds of hospitalization if they become infected with COVID-19. More effective medical support and interventions for these vulnerable populations are re-quired to reduce inequity in healthcare utilization and alleviate the burden on healthcare systems.
ABSTRACT
This article combines automated scraping of Weibo data and a critical discourse analysis to examine the ways in which online anti-African sentiments produce and amplify the interrelations of racial stigma, sexism and homophobia, as well as misinformation about infectious disease on Chinese social media. The paper finds that three nodal points strongly unite the online anti-African discourse: one, 'unrestrained and promiscuous' African men are carrying the viruses (such as AIDS and/or COVID-19);two, 'unchaste' Chinese women (and occasionally gay men) are receiving the virus;three, there is unidirectional transmission of these viruses from Africans to Chinese. Further, our research findings point to complicated and ambiguous relations between online racist sentiments, state censorship, and China-Africa relations.
ABSTRACT
Lung segmentation on CT images is a crucial step for a computer-aided diagnosis system of lung diseases. The existing deep learning based lung segmentation methods are less efficient to segment lungs on clinical CT images, especially that the segmentation on lung boundaries is not accurate enough due to complex pulmonary opacities in practical clinics. In this paper, we propose a boundary-guided network (BG-Net) to address this problem. It contains two auxiliary branches that seperately segment lungs and extract the lung boundaries, and an aggregation branch that efficiently exploits lung boundary cues to guide the network for more accurate lung segmentation on clinical CT images. We evaluate the proposed method on a private dataset collected from the Osaka university hospital and four public datasets including StructSeg [1], HUG [2], VESSEL12 [3], and a Novel Coronavirus 2019 (COVID-19) dataset [4]. Experimental results show that the proposed method can segment lungs more accurately and outperform several other deep learning based methods.