Audiovestibular adverse events following COVID-19 vaccinations (preprint)

ImportanceEvidence regarding audiovestibular adverse events post COVID-19 vaccination to date has been inconclusive regarding a potential etiological association. This study used a multi-data source approach to assess incidence of these events following COVID-19 vaccination. ObjectiveTo determine if there was an increase in audiovestibular adverse events following COVID-19 vaccination in South-eastern Australia during January 2021 - March 2023. DesignRetrospective observational analysis of spontaneous reports of audiovestibular events to a statewide vaccine safety surveillance service, SAEFVIC, as well as accompanying self-controlled case series (SCCS) analysis using general practice data collected via the POpulation Level Analysis and Reporting (POLAR) tool with permission from Primary Health Networks (PHNs) as the de-identified dataset owners in Victoria and New South Wales. SettingVictoria and New South Wales (NSW), Australia. ParticipantsVictorians who spontaneously reported an audiovestibular-related symptom or diagnosis to SAEFVIC, and people in Victoria and NSW who presented to a POLAR GP registered practice with a new audiovestibular diagnosis. ExposuresCOVID-19 vaccination with adenovirus vector, mRNA or protein-subunit vaccine. Outcomes and MeasuresIn SAEFVIC, audiovestibular events of interest were ascertained through searching key words in the vaccine safety database. Reporting rates were calculated and compared per 100,000 COVID-19 vaccine doses administered and recorded in the Australian Immunisation Register (AIR). Audiovestibular presentations of interest were isolated from the general practice dataset aggregated by POLAR, by searching for relevant SNOMED CT codes. Similarly, relative incidence (RI) was calculated for all COVID-19 vaccine types. ResultsThis study demonstrates an increase in general practice presentations of vertigo following mRNA vaccines (RI= 1.40 P <.001), and tinnitus following both the adenovirus vector and mRNA vaccines (RI= 2.25, P <.001 and 1.53, P <.001 respectively). There was no increase in hearing loss following any COVID-19 vaccinations. Conclusions and RelevanceThis is the first study that demonstrates an increase in audiovestibular presentations following COVID-19 vaccination, in particular, vertigo and tinnitus. Healthcare providers and vaccinees should be alert to potential audiovestibular complaints after COVID-19 vaccination. Our analysis highlights the importance of using large real-world datasets to gather reliable evidence for public health decision making. KEY POINTSO_ST_ABSQuestionC_ST_ABSIs there an increase in audiovestibular adverse events after COVID-19 vaccination (adenovirus vector [AstraZenecas Vaxzervria(R) ChadOx1-S], mRNA [Pfizer-BioNTechs Comirnaty(R) BNT162b2 and Modernas Spikevax(R)] or protein-subunit [Novavaxs Nuvaxovid(R)])? FindingsThis Australian study using spontaneous surveillance reports and large-scale general practice data, found an increase in incidence related to vertigo following mRNA vaccines (Relative Incidence = 1.40, P <.001), and tinnitus following both adenovirus vector and mRNA vaccines (Relative Incidence = 2.25, P <.001 and 1.53, P <.001 respectively). No increase in hearing loss following vaccination was observed. MeaningHealthcare providers and vaccinees should be alert to potential audiovestibular complaints following COVID-19 vaccination.

Menstrual cycle changes increased following COVID-19 mRNA vaccination: Social media validation and self-controlled case series analysis. (preprint)

ObjectivesTo investigate if there was an increase in menstrual abnormality related presentation post COVID-19 vaccination. DesignBERTopic machine learning, with a guided topic modelling option was used to analyse mentions of menstrual change in relation to COVID-19 vaccination on the social media platform Reddit. Self-controlled case series (SCCS) analysis using general practice data collected via the POpulation Level Analysis and Reporting (POLAR) tool with permission from Primary Health Networks (PHNs) as the de-identified dataset owners in Victoria and New South Wales. SettingGlobally for social media analysis. Victoria and New South Wales (NSW), Australia for POLAR ParticipantsFor social media analysis, people who made a Reddit post about menstrual concerns post COVID-19 vaccine. For the SCCS analysis, people who presented to a POLAR GP registered practice with a new menstrual abnormality diagnosis. ExposuresCOVID-19 vaccination with adenovirus vector [AstraZenecas Vaxzervria(R) ChadOx1-S], mRNA [Pfizer-BioNTechs Comirnaty(R) BNT162b2 and Modernas Spikevax(R)] or protein-subunit [Novavaxs Nuvaxovid(R)]). Outcomes and MeasuresScraped social media posts were pre-processed, analysed for positive, negative, and neutral sentiments and topic modelled. Menstrual abnormality presentations of interest were isolated from the general practice dataset aggregated by POLAR, by searching for relevant SNOMED CT codes. Similarly, relative incidence (RI) was calculated for all COVID-19 vaccine types. ResultsSocial media analysis saw peaks in menstrual change posts on Reddit since the global COVID-19 vaccine rollout. The SCCS analysis demonstrates an increase in general practice presentations of menstrual abnormality diagnosis following mRNA vaccines (RI= 1.14, 95% CI: 1.07 to 1.22, P <0.001). Conclusions and RelevanceThis study demonstrates an increase in menstrual abnormality presentations following COVID-19 mRNA vaccination. Our findings validate the concerns raised on social media so people who are vaccinated or are considering future vaccines feel heard, supported, and validated. Our analysis highlights the importance of using large real-world datasets to gather reliable evidence for public health decision making. Summary box O_TEXTBOXSection 1: What is already known on this topic?O_LISurveys and spontaneous surveillance systems suggested and association of menstrual cycle changes with COVID-19 vaccination. C_LIO_LIHeavy menstrual bleeding was added to the product information for mRNA vaccines in the European Union C_LI Section 2: What this study adds?O_LIOur study is the first to prove an increase in menstrual abnormality related presentations post mRNA COVID-19 vaccines using routinely collected general practice data. C_LIO_LIOur findings validate the concerns raised by people who menstruate and help them with their future decision to vaccinate. C_LI C_TEXTBOX

Challenges in Dental Care of Children with Visual and Auditory Impairment – A Questionnaire Survey

Background: Dental care is the most common unmet health care need of disabled children. Hearing and visually impaired special children are at a greater risk for developing oral health problems and pose unique difficulties in their dental treatment. The COVID-19 pandemic seems to be the biggest challenge that special care dental services have ever faced. Aim: This study was undertaken with the aim of assessing the views and perception of dentists regarding the treatment needs and the challenges faced by visually impaired and hearing-impaired children during the pandemic. Methodology: A total of 402 dental practitioners participated in the study out of which 133 were BDS graduates and 269 were MDS graduates. An online cross-sectional survey using Google forms was formulated and all practitioners were given 18 close-ended questionnaires pertaining to the knowledge and awareness regarding the impact of COVID-19 on the dental treatment of hearing and visually impaired children. Results: The results of our study showed that 87.6% of the practitioners were aware regarding the impact of COVID-19 on hearing and visually impaired children and 80.3% of them believe that the dental appointments of disabled children decreased during post COVID time. Conclusion: Results of our study showed majority of the practitioners were aware of the methods that can be adapted in this pandemic to reduce the barriers in the treatment of specially-abled children.

"Swastha-Shwasa": Utility of Deep Learning for Diagnosis of Common Lung Pathologies from Chest X-Rays

Respiratory diseases are one of the leading causes of death and disability in the world. Integration of AI with existing Chest X-Ray (CXR) diagnostics is currently a hot research topic. On similar lines, we propose a technique termed "Swasta-shwasa" for multi-class classification that associates CXR with one among Tuberculosis, COVID-19, Viral pneumonia, Bacteria Pneumonia, Normal and Lung Opacity ailments based on Deep Learning. The proposed technique which has accomplished an overall 98% test accuracy, 0.9991 AUROC, average Specificity of 99.82% and average Sensitivity of 98.51% involves four stages: Pre-processing, Segmentation, Classification and Saliency map visualization. Further, the trained model is used to predict on unseen real life data of COVID-19 cases from India and a cross-population generalization accuracy of 85% is witnessed. XAI is augmented for model interpretability. We also explore why CLAHE may not be suitable choice for pre-processing of CXRs.