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Global estimates of visual impairment: 2010
Abstract
Abstract Background Long COVID is a post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection syndrome characterised by not recovering for several weeks or months following the acute episode. The effectiveness of COVID-19 vaccines against long-term symptoms of COVID-19 is not well understood. We determined whether vaccination was associated with the incidence of reporting long-term symptoms post-SARS-CoV-2 infection Methods We invited individuals who were PCR tested for SARS-CoV-2 infection at participating hospitals between March 2020-November 2021 to fill an online questionnaire that included baseline demographics, details of their acute episode and information about symptoms they were currently experiencing. Using binomial regression, we compared vaccinated individuals with those unvaccinated and those uninfected in terms of self-reported symptoms post-acute infection. Results We included 951 infected and 2437 uninfected individuals. Of the infected, 637(67%) were vaccinated. The most commonly reported symptoms were; fatigue (22%), headache (20%), weakness (13%), and persistent muscle pain (10%). After adjusting for follow-up time and baseline symptoms, those who received two doses less likely than unvaccinated individuals to report any of these symptoms by 64%, 54%, 57%, and 68% respectively, (Risk ratios 0.36, 0.46, 0.43, 0.32, p<0.04 in the listed sequence). Those who received two doses were no more likely to report any of these symptoms than individuals reporting no previous SARS-CoV-2 infection. Conclusions Vaccination with at least two doses of COVID-19 vaccine was associated with a substantial decrease in reporting the most common post-acute COVID-19 symptoms, bringing it back to baseline. Our results suggest that, in addition to reducing the risk of acute illness, COVID-19 vaccination may have a protective effect against long COVID.
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Cited by
Flaxman, S. R., Bourne, R. R. A., Resnikoff, S., Ackland, P., Braithwaite, T., Cicinelli, M. V., Das, A., Jonas, J. B., Keeffe, J., Kempen, J. H., et al. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30393-5 · Cited by 3,477
Artificial intelligence and deep learning in ophthalmology
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313173 · Cited by 1,554
Wallace, T. C., Bailey, R. L., Blumberg, J. B., Burton-Freeman, B., Chen, C.-Y. O., Crowe-White, K. M., Drewnowski, A., Hooshmand, S., Johnson, E., Lewis, R., et al. (2020) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2019.1632258 · Cited by 638
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., et al. (2021) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2020.100900 · Cited by 626
Martínez-Pérez, B., de la Torre-Díez, I., & López-Coronado, M. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2600 · Cited by 550
Metadata
- Authors
- Pascolini, D., & Mariotti, S. P.
- Venue
- British Journal of Ophthalmology
- Publisher
- BMJ
- Published
- 2011-12-01
- Volume
- 96
- Issue
- 5
- Pages
- 614-618
- ISSN
- 0007-1161 (ISSN-L)
- 1468-2079
- OpenAlex
- W2123215356
- Cited by
- 3,763
- References
- 0
- FWCI
- 74.27
- Type
- journal article
- Language
- en
- Open access
- closed
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