Persistent olfactory dysfunction 2 years after onset of COVID-19

Sensing Sociality: Disruptions of Social Life When Living With Chemosensory Dysfunctions After COVID-19

Taste and smell are of direct importance in most social interactions. Radical disruptions in these senses can, therefore, substantially disrupt sociality. This paper focuses on the experiences of a particular type of disruption: persistent chemosensory dysfunctions after COVID-19. We conducted semi-structured interviews with 30 patients undergoing treatment for chemosensory dysfunctions and analyzed the ways in which their experiences have influenced social relations and activities, particularly regarding food and eating. The findings reveal that these dysfunctions have made the participants markedly aware that food and eating are pivotal to full participation in social life. As is smell, both surrounding smells and the perception of one's own smell, with dysfunctions leading to several social consequences. Such problems are handled through both avoidance behavior and adaptations. While adaptations facilitate interactions, they come at the cost of feeling a burden to others or not fully appreciating an event (e.g., a shared meal). Social support is of great importance, ranging from minor practical assistance, such as a friend checking if the milk is sour, to the profound emotional relief felt from empathic treatment and recognition that the problems are real. Here, healthcare professionals can play a vital role, even in the (perceived) absence of clinical effectiveness of the treatment. The experiences expressed are partially in line with other manifestations of Long COVID and with chemosensory dysfunctions due to other illnesses, but only partially, since this is a patient group with needs and experiences that are unique, in that sociality is so strongly affected solely by disruptions in sensory abilities.

Incidence and characteristics of otitis media with effusion in adults before, during, and after the COVID-19 pandemic

OBJECTIVES

To investigate the incidence and characteristics of adult otitis media with effusion (OME) before, during, and after the COVID-19 pandemic.

METHODS

A retrospective descriptive study was conducted. The incidence, age, sex, affected ear side, time of OME onset according to COVID-19 and days of improvement after conservative treatment were determined to assess the clinical features of adult OME in different periods of the COVID-19 pandemic.

RESULTS

The incidence of adult OME during these periods was 3.17%, 2.30%, 6.18%, and 3.68%, respectively. Unilateral ear involvement and male sex were more common. The onset of adult OME occurred 7.80 ± 3.97 days after COVID-19 diagnosis, and improvement was observed after 12.24 ± 5.08 days of conservative treatment. Patients in the post-pandemic period were older than those in the non-pandemic period.

CONCLUSION

The incidence of adult OME in China showed a tendency to decrease, recover, and decrease again following the COVID-19 outbreak. Pandemic prevention and control measures have had a certain impact on reducing the incidence, but the elderly are more prone to this disease.

An olfactory perceptual fingerprint in people with olfactory dysfunction due to COVID-19

The sense of smell is based on sensory detection of the molecule(s), which is then further perceptually interpreted. A possible measure of olfactory perception is an odor-independent olfactory perceptual fingerprint (OPF) defined by Snitz et al. We aimed to investigate whether OPF can distinguish patients with olfactory dysfunction (OD) due to coronavirus disease (COVID-19) from controls and which perceptual descriptors are important for that separation. Our study included 99 healthy controls and 41 patients. They rated 10 odors using 8 descriptors such as "pleasant," "intense," "familiar," "warm," "cold," "irritating," "edible," and "disgusting." An unsupervised machine learning method, hierarchical cluster analysis, showed that OPF can distinguish patients from controls with an accuracy of 83%, a sensitivity of 51%, and a specificity of 96%. Furthermore, a supervised machine learning method, random forest classifier, showed that OPF can distinguish patients and controls in the testing dataset with an accuracy of 86%, a sensitivity of 64%, and a specificity of 96%. Principal component analysis and random forest classifier showed that familiarity and intensity were the key qualities to explain the variance of the data. In conclusion, people with COVID-19-related OD have a fundamentally different olfactory perception.