Estimates of COVID-19 Risk Factors among Social Strata and Predictors for a Vulnerability to the Infection.
Dimitra S MouliouOurania S KotsiouKonstantinos I GourgoulianisPublished in: International journal of environmental research and public health (2021)
Coronavirus disease 2019 (COVID-19) has emerged as a potentially severe disease, especially for individuals presenting with certain underlying medical conditions. We analyzed the rates of comorbidities and symptoms to reveal the potential severity of the pandemic in Volos, one of the most air-polluted cities in Greece. Environmental and health-related predictors for SARS-CoV-2 infection were investigated. A web-based questionnaire was disseminated through social media in the first half of March 2021 during a five-month strict lockdown. Sociodemographic data, preexisting medical conditions, frequency of clinical symptoms, and COVID-19 information were recorded. The study population consisted of 2000 responders. Four-fifths of the participants reported comorbidities that could increase vulnerability to severe COVID-19. Respiratory symptoms were reported from the unemployed and from retirees, and cold-related symptoms were reported in the education sector and in undergraduates. Women and younger generations shaped social vulnerability to respiratory infections similar to the elderly. SARS-CoV-2 infection was reported in 3.7% of the study population. Common headache (OR 2; CI 1189-3013; p = 0.007) and prior pneumonia (OR 1.9; CI 1024-2898; p = 0.04) were significant predictors for susceptibility to SARS-CoV-2 infection. The importance of monitoring society through community-based questionnaires is highlighted, for predicting and preventing future widespread transmission of infectious diseases.
Keyphrases
- coronavirus disease
- respiratory syndrome coronavirus
- sars cov
- social media
- healthcare
- climate change
- risk factors
- infectious diseases
- health information
- early onset
- quality improvement
- human health
- heavy metals
- genome wide
- metabolic syndrome
- drug induced
- risk assessment
- machine learning
- pregnant women
- cross sectional
- dna methylation
- mass spectrometry
- depressive symptoms
- respiratory tract
- artificial intelligence
- data analysis
- skeletal muscle
- acute respiratory distress syndrome
- respiratory failure