Burden and prevalence of risk factors for severe COVID-19 disease in the ageing European population - A SHARE-based analysis.
Linda Juel AhrenfeldtCamilla Riis NielsenSören MöllerKaare ChristensenRune Lindahl-JacobsenPublished in: Research square (2020)
Aim: International health authorities suggest that individuals aged 65 years and above and people with underlying comorbidities such as hypertension, chronic lung disease, cardiovascular disease, cancer, diabetes, and obesity are at increased risk of severe Coronavirus Disease 2019 (COVID-19); however, the prevalence of risk factors is unknown in many countries. Therefore, we aim to describe the distribution of these risk factors across Europe. Subject and Methods: Prevalence of risk factors for severe COVID-19 was identified based on interview for 73,274 Europeans aged 50+ participating in the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2017. Burden of disease was estimated using population data from Eurostat. Results: A total of 75.3% of the study population (corresponding to app. 60 million European men and 71 million women) had at least one risk factor for severe COVID-19, 45.9% (app. 36 million men and 43 million women) had at least two factors and 21.2% (app. 17 million men and 20 million women) had at least three risk factors. The prevalences of underlying medical conditions ranged from 4.5% for cancer to 41.4% for hypertension, and the region-specific prevalence of having at least three risk factors ranged from 18.9% in Northern Europe to 24.6% in Eastern Europe. Conclusions: Information about the prevalences of risk factors might help authorities to identify the most vulnerable subpopulations with multiple risk factors of severe COVID-19 disease and thus to decide appropriate strategies to mitigate the pandemic.
Keyphrases
- risk factors
- coronavirus disease
- sars cov
- cardiovascular disease
- healthcare
- respiratory syndrome coronavirus
- polycystic ovary syndrome
- early onset
- type diabetes
- blood pressure
- public health
- metabolic syndrome
- mental health
- health information
- drug induced
- machine learning
- middle aged
- coronary artery disease
- pregnant women
- health promotion
- data analysis