COVID-19 and the selection problem in national cause-of-death statistics

The causal relationship model of factors influencing COVID-19 preventive behaviors during the post-pandemic era and implications for health prevention strategies: a case of Bangkok City, Thailand

BACKGROUND

Though, many countries are currently in the COVID post-pandemic era, people's health protective behaviours are still essential to protect their health and well-being. This study aims to evaluate people's understanding and perceptions of COVID-19 risk characteristics (i.e. threat occurrence, threat severity, perceived susceptibility and exposure), the health risk perception towards COVID-19, and health protective behaviours. The study also aims to estimate the associations among these factors by the analysis of structural equation modelling (SEM).

METHODS

From 15 October to 9 November 2022, questionnaire surveys were administrated to 521 people living in Bangkok of Thailand by using the convenience sampling technique. The analyses were carried out in three phases including descriptive statistical analyses, a measurement model assessment using a confirmatory factor analysis (CFA), and structural equation modelling (SEM) analysis.

RESULTS

The results of descriptive analyses demonstrated that the majority of respondents, 39.9%, had the age between 20 and 30 years old, and 61.4% of them were female. Approximately 52.1% of them had a bachelor's degree. Upon analysing individuals' understanding and perceptions of all risk characteristics, individuals' understanding of COVID-19 severity did not statistically affect health risk perception towards COVID-19, whereas perceived exposure had the strongest effect and in turn influenced health protective behaviours. Perceived susceptibility and understanding of the threat occurrence also significantly affected health risk perception, and indirectly affected health protective behaviours.

CONCLUSIONS

This study implies that though the potential health impact of COVID-19 is perceived as less severe, people can still construct a perception of its risk particularly based on their perceived exposure and susceptibility. Thus, communicating people about exposure conditions and susceptibility can greatly contribute to people' construction of risk perception towards COVID-19 which subsequently leads to the decision to perform health protective behaviours.

Excess mortality and COVID-19 deaths in Italy: A peak comparison study

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not by default excess deaths, because many COVID-19 deaths were not correctly registered, with most being attributable to other causes and to the overall crisis conditions; and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period of January 5, 2020-October 31, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks, and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths, provides further evidence in favor of a positive correlation between COVID-19 deaths and all-cause excess mortality.

COVID-19-related excess mortality - an overview of the current evidence

Analysis of excess deaths, defined as the difference in the total number of deaths in an emergency compared to the number of deaths expected under normal conditions, allows a more reliable assessment of the impact on health systems caused by the global threat of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2). So far, data for the two years of the pandemic (2020-2021) indicates the occurrence of 14.9 million excess deaths according to WHO (World Health Organization) estimates. The purpose of the analysis conducted was to define the concept and identify the causes of excess mortality during the COVID-19 pandemic. Inconsistent and unreliable death registration systems; overburdened health systems in low- and middle-income countries; reduced access to medical services for patients with health problems other than COVID-19; the introduction of social distancing and lockdown rules, which translated into increased deaths from psychiatric illnesses and addictions; political considerations and media messages that interfered with vaccination acceptance and adherence; and the additional impact of other natural disasters (hurricanes, floods, drought) were identified as the most important reasons for excess deaths occurrence. The correct identification of country-specific factors and the correct response and countermeasures taken appear crucial in terms of limiting the negative impact of the current pandemic, but also of future threats of a similar nature, in order to reduce excess deaths.

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.