Epidemiological Characteristics of COVID-19 Cases in Non-Italian Nationals in Sicily: Identifying Vulnerable Groups in the Context of the COVID-19 Pandemic in Sicily, Italy.
Palmira ImmordinoDario GenoveseFatima MoralesAlessandra CasuccioEmanuele AmodioPublished in: International journal of environmental research and public health (2022)
As in other parts of the world, undocumented migrants in Italy suffer worse health status due to their immigration enforcement situation and other vulnerabilities such as precarious illegal jobs, exploitation and abuse or barriers to higher education, with higher prevalence of chronic noncommunicable diseases. The COVID-19 pandemic, as other pandemics, has not affected everyone equally. The undocumented was one of the most affected groups with regard to hospitalization rates and mortality worldwide. Sicily is one of the gates of entrance to Europe for migrants and asylum seekers from Africa and Asia. Herein, we described the epidemiological characteristics of COVID-19 cases in Sicily to compare hospitalization rate and mortality between Italian nationals and foreigners. We extracted data from the integrated national surveillance system established by the Italian National Institute of Health (Istituto Superiore di Sanità, ISS) to collect information on all COVID-19 cases and deaths in Sicily. We found that the hospitalization rates were higher in undocumented foreigners, and they were most likely to present a more severe clinical outcome compared to Italian nationals. Inclusive public health policies should take this population group into consideration to achieve the Health for All goal.
Keyphrases
- public health
- coronavirus disease
- sars cov
- healthcare
- quality improvement
- risk factors
- global health
- health information
- cardiovascular events
- mental health
- cardiovascular disease
- electronic health record
- big data
- escherichia coli
- machine learning
- coronary artery disease
- risk assessment
- staphylococcus aureus
- social media
- pseudomonas aeruginosa
- human health
- data analysis