Action is needed to tackle the clinical, psychological and socioeconomic impact of perinatal COVID-19.
Despina D BrianaVassiliki PapaevangelouAriadne Malamitsi-PuchnerPublished in: Acta paediatrica (Oslo, Norway : 1992) (2022)
The COVID-19 pandemic has turned perinatal healthcare into a worldwide public health challenge. Although initial data did not demonstrate pregnancy as a more susceptible period to adverse outcomes of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, an increasing number of reports now certify maternal illness as a high-risk condition for the development of maternal-fetal complications. Despite the rarity of SARS-CoV-2 vertical transmission, severe maternal illness might induce adverse perinatal and neonatal outcomes. Additionally, perinatal COVID-19 data may raise concerns about long-term harmful consequences to the offspring in the framework of non-communicable diseases. The World Health Organisation, as well as scientific literature, consider the protection of the maternal-fetal dyad against COVID-19 as a critical issue and, therefore, strongly promote and encourage the vaccination of pregnant and lactating women. Furthermore, the pandemic has triggered an unprecedented recession, leading to historic levels of unemployment and deprivation, while health, societal, economic and gender inequities particularly affecting low-income and middle-income countries, have increased. This mini-review provides an updated brief report on historical, clinical, psychological and socioeconomic aspects of the COVID-19 pandemic based on 10 lectures presented at the 9th Maria-Delivoria-Papadopoulos Perinatal Symposium, held virtually on 19 March 2022.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- pregnancy outcomes
- pregnant women
- coronavirus disease
- public health
- healthcare
- birth weight
- mental health
- health information
- electronic health record
- big data
- emergency department
- physical activity
- risk factors
- weight gain
- artificial intelligence
- high fat diet
- global health
- social media
- machine learning
- body mass index
- deep learning
- adipose tissue
- metabolic syndrome