Asymptomatic COVID-19 in the elderly: dementia and viral clearance as risk factors for disease progression.

Esteban, Ignacio; Bergero, Georgina; Alves, Camila; Bronstein, Micaela; Ziegler, Valeria; Wood, Cristian; Caballero, Mauricio T.; Wappner, Diego; Libster, Romina; Marc, Gonzalo Pérez; Polack, Fernando P.

doi:10.12688/gatesopenres.13357.2

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…However, on closer analysis, we found all asymptomatic infections in this age group were in cases aged over 70, while the symptomatic patients ranged in age from 65 to 68 years, with a mean age difference of 6.1 years (95% CI 3.7-8.5, p < 0.001) between those with symptoms and those without. This is consistent with reports of higher asymptomatic presentations of infections in older populations over 75 years or with comorbidity, where 65-68% of cases failed to develop symptoms [19,20].…”

Section: Discussionsupporting

confidence: 92%

Demographic Characteristics of Unvaccinated Asymptomatic and Symptomatic SARS-CoV-2 Cases in Barwon South West, Victoria, Australia

Tobin-Salzman,

Cooper,

McNamara

et al. 2023

Pathogens

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We investigated 328 SARS-CoV-2 cases in Barwon South West, Victoria, Australia, in the 2020 pre-vaccination period, comparing infections with symptoms to those that remained asymptomatic. De-identified self-reported data on case characteristics and symptom progression from three sequential questionnaires were examined. Multivariable logistic regression was used to model associations between demographic profiles and symptoms. Asymptomatic infections were more than three times as likely to be seen in ethnic minority groups than the Caucasian population after adjusting for gender and age [OR 3.2, 95% CI 1.5–6.7, p < 0.01] and were more common among cases of Asian background [OR 2.8, 95%CI 1.2–6.4]. Asymptomatic infections were also more common in youth and younger adults, but cases were approximately seven times more likely to be in seniors (≥65 years) compared with those 24 years of age or younger after adjusting for sex and ethnicity [OR 6.9, 95% CI 1.3–35.8]. The overrepresentation of ethnic minority groups among asymptomatic infections is suggestive of genetic haplotype variability by ethnic group, conferring greater cross-protection from other coronaviruses in the initial phase of the COVID-19 pandemic. Replication of this analysis in the post-vaccination era and reassessment of symptom expression according to ethnicity in a community with established vaccine and infection-induced immunity would determine whether this is a sustained association or one confined to the early stages of a pandemic in an immunologically naive population. These findings may, in part, reflect differences in testing patterns by ethnicity and true differences in disease expression, both of which are important to understand in order to inform transmission prevention strategies and tailored risk messaging according to ethnic background.

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Section: Discussionsupporting

confidence: 92%

Demographic Characteristics of Unvaccinated Asymptomatic and Symptomatic SARS-CoV-2 Cases in Barwon South West, Victoria, Australia

Tobin-Salzman,

Cooper,

McNamara

et al. 2023

Pathogens

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“…Dementia may further be a risk factor for the spread of the virus, since adherence to sanitary rules and physical distancing measures is very difficult for these patients due to their cognitive state and limited possibilities to cope with changing circumstances [ 36 ]. Additionally, a substantial number of the elder people with SARS-CoV-2 infection is asymptomatic or has atypical symptoms [ 5 , 6 , 37 ], which therefore make the diagnosis difficult, especially in those with dementia.…”

Section: Discussionmentioning

confidence: 99%

Exploratory study of risk factors related to SARS-CoV-2 prevalence in nursing homes in Flanders (Belgium) during the first wave of the COVID-19 pandemic

Janssens,

Heytens,

Meyers

et al. 2023

PLoS ONE

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In a previous study in Belgian nursing homes (NH) during the first wave of the COVID-19 pandemic, we found a SARS-CoV-2 seroprevalence of 17% with a large variability (0–45%) between NH. The current exploratory study aimed to identify nursing home-specific risk factors for high SARS-CoV-2 seroprevalence. Between October 19th, 2020 and November 13th, 2020, during the second COVID-19 wave in Belgium, capillary blood was collected on dried blood spots from 60 residents and staff in each of the 20 participating NH in Flanders and Brussels. The presence of SARS-CoV-2-specific IgG antibodies was assessed by ELISA. Risk factors were evaluated using a questionnaire, filled in by the director or manager of the NH. Assessed risk factors comprised community-related factors, resident-related factors, management and performance features as well as building-related aspects. The relation between risk factors and seroprevalence was assessed by applying random forest modelling, generalized linear models and Bayesian linear regression. The present analyses showed that the prevalence of residents with dementia, the scarcity of personal protective equipment (surgical masks, FFP2 masks, glasses and face shields), and inadequate PCR test capacity were related to a higher seroprevalence. Generally, our study put forward that the various aspects of infection prevention in NH require more attention and investment. This exploratory study suggests that the ratio of residents with dementia, the availability of test capacity and personal protective equipment may have played a role in the SARS-CoV-2 seroprevalence of NH, after the first wave. It underscores the importance of the availability of PPE and education in infection prevention. Moreover, investments may also yield benefits in the prevention of other respiratory infections (such as influenza).

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“…This was commonly reported by people during the period of COVID. Some COVID patients went through the disease with severe symptoms despite being in the best of health before infection, while some chronically ill elderly people showed no symptoms ( Zhang et al, 2020 ; Esteban et al, 2021 ). The host response following the exposure is linked to genetic predisposition, disruption of the individual’s microbiome ( Pichon, Lina & Josset, 2017 ), being in high-risk group ( Walker et al, 2022 ) and effective immune surveillance.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

Işık

Aydın

2023

PeerJ

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Respiratory diseases are among the major health problems causing a burden on hospitals. Diagnosis of infection and rapid prediction of severity without time-consuming clinical tests could be beneficial in preventing the spread and progression of the disease, especially in countries where health systems remain incapable. Personalized medicine studies involving statistics and computer technologies could help to address this need. In addition to individual studies, competitions are also held such as Dialogue for Reverse Engineering Assessment and Methods (DREAM) challenge which is a community-driven organization with a mission to research biology, bioinformatics, and biomedicine. One of these competitions was the Respiratory Viral DREAM Challenge, which aimed to develop early predictive biomarkers for respiratory virus infections. These efforts are promising, however, the prediction performance of the computational methods developed for detecting respiratory diseases still has room for improvement. In this study, we focused on improving the performance of predicting the infection and symptom severity of individuals infected with various respiratory viruses using gene expression data collected before and after exposure. The publicly available gene expression dataset in the Gene Expression Omnibus, named GSE73072, containing samples exposed to four respiratory viruses (H1N1, H3N2, human rhinovirus (HRV), and respiratory syncytial virus (RSV)) was used as input data. Various preprocessing methods and machine learning algorithms were implemented and compared to achieve the best prediction performance. The experimental results showed that the proposed approaches obtained a prediction performance of 0.9746 area under the precision-recall curve (AUPRC) for infection (i.e., shedding) prediction (SC-1), 0.9182 AUPRC for symptom class prediction (SC-2), and 0.6733 Pearson correlation for symptom score prediction (SC-3) by outperforming the best leaderboard scores of Respiratory Viral DREAM Challenge (a 4.48% improvement for SC-1, a 13.68% improvement for SC-2, and a 13.98% improvement for SC-3). Additionally, over-representation analysis (ORA), which is a statistical method for objectively determining whether certain genes are more prevalent in pre-defined sets such as pathways, was applied using the most significant genes selected by feature selection methods. The results show that pathways associated with the ‘adaptive immune system’ and ‘immune disease’ are strongly linked to pre-infection and symptom development. These findings contribute to our knowledge about predicting respiratory infections and are expected to facilitate the development of future studies that concentrate on predicting not only infections but also the associated symptoms.

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Asymptomatic COVID-19 in the elderly: dementia and viral clearance as risk factors for disease progression.

Cited by 7 publications

References 26 publications

Demographic Characteristics of Unvaccinated Asymptomatic and Symptomatic SARS-CoV-2 Cases in Barwon South West, Victoria, Australia

Demographic Characteristics of Unvaccinated Asymptomatic and Symptomatic SARS-CoV-2 Cases in Barwon South West, Victoria, Australia

Exploratory study of risk factors related to SARS-CoV-2 prevalence in nursing homes in Flanders (Belgium) during the first wave of the COVID-19 pandemic

Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity

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