Examining the Prevalence of Long-Covid Symptoms: A Cross-Sectional Study

Authors

DOI:

https://doi.org/10.22399/ijcesen.243

Keywords:

Covid-19, Long Covid, ChronicCovid Sydrome, SARS-CoV-2

Abstract

Background: It’s increasingly recognized that SARS-CoV-2 can produce long-term chronic complications after recovering from the acute effects of the infection. But little is known about the prevalence, risks, or whether it’s possible to predict a long-term course of the disease in the early stages, the resulting quality of life disorder. In this study, the effects of chronic Covid-19 syndromes (CCS) on type, prevalence, quality of life after recovery in Covid-19 patients were investigated.

Methods: Four weeks after the quarantine period of the patients was completed, a cross-sectional study was conducted with a questionnaire on people reached via Google forms to determine the symptoms of long-Covid.

Results: 1044 people over the age of 18, who aren’t pregnant, and who have had Covid-19 were included in the analysis. It was determined that 65.6% (n=685) of the participants continued to have symptoms after the PCR test was negative/after they recovered. It was concluded that myalgia, fatigue, joint pain, anosmia was observed in 76.4% (n=797) of the individuals participating in our study.

Conclusion: It has been observed that people who have had Covid-19 commonly show additional or ongoing symptoms and associated impairment of quality of life in the short term. It was determined that individuals who initially had a symptom of shortness of breath or lung involvement were more likely to develop long-term symptoms. More importantly, our study revealed that the overall disease level is an important variable that should be considered when assessing the statistical significance of symptoms associated with Covid-19.

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Published

2024-02-27

How to Cite

Say, A., Çakır, D., AVRAMESCU, T., USTUN, G., NEAGOE, D., KAHVECİ, M., … KOMOREK, J. (2024). Examining the Prevalence of Long-Covid Symptoms: A Cross-Sectional Study. International Journal of Computational and Experimental Science and Engineering, 10(1). https://doi.org/10.22399/ijcesen.243

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Research Article