Tinnitus after COVID-19 vaccination: Findings from the vaccine adverse event reporting system and the vaccine safety datalink.

PURPOSE: To assess the occurrence of tinnitus following COVID-19 vaccination using data mining and descriptive analyses in two U.S. vaccine safety surveillance systems. METHODS: Reports of tinnitus after COVID-19 vaccination to the Vaccine Adverse Event Reporting System (VAERS) from 2020 through 2024 were examined using empirical Bayesian data mining and by calculating reporting rates. In the Vaccine Safety Datalink (VSD) population, ICD-10 coded post-vaccination medical visits were examined using tree-based data mining, and tinnitus visit incidence rates during post-vaccination days 1-140 were calculated by age group for COVID-19 vaccines and for comparison, influenza vaccine. RESULTS: VAERS data mining did not find disproportionate reporting of tinnitus for any COVID-19 vaccine. VAERS received up to 84.82 tinnitus reports per million COVID-19 vaccine doses administered. VSD tree-based data mining found no signals for tinnitus. VSD tinnitus visit incidence rates after COVID-19 vaccines were similar to those after influenza vaccine except for the group aged ≥65 years (Moderna COVID-19 vaccine, 165 per 10,000 person-years; Pfizer-BioNTech COVID-19 vaccine, 154; influenza vaccine, 135). CONCLUSIONS: Overall, these findings do not support an increased risk of tinnitus following COVID-19 vaccination but cannot definitively exclude the possibility. Descriptive comparisons between COVID-19 and influenza vaccines were limited by lack of adjustment for potential confounding factors.

Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol.

BACKGROUND: Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. METHODS: To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. DISCUSSION: Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

COVID-19 Vaccination Coverage and Factors Associated With Vaccine Uptake Among People With HIV.

Importance: People with HIV (PWH) may be at increased risk for severe outcomes with COVID-19 illness compared with people without HIV. Little is known about COVID-19 vaccination coverage and factors associated with primary series completion among PWH. Objectives: To evaluate COVID-19 vaccination coverage among PWH and examine sociodemographic, clinical, and community-level factors associated with completion of the primary series and an additional primary dose. Design, Setting, and Participants: This retrospective cohort study used electronic health record data to assess COVID-19 vaccination information from December 14, 2020, through April 30, 2022, from 8 health care organizations of the Vaccine Safety Datalink project in the US. Participants were adults diagnosed with HIV on or before December 14, 2020, enrolled in a participating site. Main Outcomes and Measures: The percentage of PWH with at least 1 dose of COVID-19 vaccine and PWH who completed the COVID-19 vaccine primary series by December 31, 2021, and an additional primary dose by April 30, 2022. Rate ratios (RR) and 95% CIs were estimated using Poisson regression models for factors associated with completing the COVID-19 vaccine primary series and receiving an additional primary dose. Results: Among 22 058 adult PWH (mean [SD] age, 52.1 [13.3] years; 88.8% male), 90.5% completed the primary series by December 31, 2021. Among 18 374 eligible PWH who completed the primary series by August 12, 2021, 15 982 (87.0%) received an additional primary dose, and 4318 (23.5%) received a booster dose by April 30, 2022. Receipt of influenza vaccines in the last 2 years was associated with completion of the primary series (RR, 1.17; 95% CI, 1.15-1.20) and an additional primary dose (RR, 1.61; 95% CI, 1.54-1.69). PWH with uncontrolled viremia (HIV viral load ≥200 copies/mL) (eg, RR, 0.90 [95% CI, 0.85-0.95] for viral load 200-10 000 copies/mL vs undetected or <200 copies/mL for completing the primary series) and Medicaid insurance (eg, RR, 0.89 [95% CI, 0.87-0.90] for completing the primary series) were less likely to be fully vaccinated. By contrast, greater outpatient utilization (eg, RR, 1.07 [95% CI, 1.05-1.09] for ≥7 vs 0 visits for primary series completion) and residence in counties with higher COVID-19 vaccine coverage (eg, RR, 1.06 [95% CI, 1.03-1.08] for fourth vs first quartiles for primary series completion) were associated with primary series and additional dose completion (RRs ranging from 1.01 to 1.21). Conclusions and Relevance: Findings from this cohort study suggest that, while COVID-19 vaccination coverage was high among PWH, outreach efforts should focus on those who did not complete vaccine series and those who have uncontrolled viremia.

The safety of seasonal influenza vaccination among adults prescribed immune checkpoint inhibitors: A self-controlled case series study using administrative data.

BACKGROUND: Immune checkpoint inhibitor (ICI) therapy for patients undergoing cancer treatment carries a risk of severe immune-related adverse events (IRAEs). Questions remain about whether seasonal influenza vaccination might increase the risk of developing IRAEs among these patients given that vaccines are immunomodulatory. Previous vaccine safety studies on patients with cancer prescribed ICI therapy have demonstrated conflicting results. METHODS: Using health administrative data from Ontario, Canada among adults diagnosed with cancer who had been prescribed ICI therapy and who had received an influenza vaccine from 2012 to 2019, we conducted a self-controlled case series study. The pre-vaccination control period started 42-days post-ICI initiation until 14-days prior to vaccination, the risk period was 1-42 days post-vaccination, and the post-vaccination control period was after the risk period until ICI discontinuation or a maximum period of two years. Emergency department (ED) visit(s) and/or hospitalization for any cause after ICI initiation was used to identify severe IRAEs. We fitted a fixed-effects Poisson regression model accounting for seasonality and calendar time to estimate relative incidence of IRAEs between risk and control periods. RESULTS: We identified 1133 records of cancer patients who received influenza vaccination while prescribed ICI therapy. Most were aged ≥ 66 years (73 %), were male (63 %), had lung cancer (54 %), and had received ICI therapy with a programmed cell death protein 1(PD-1) inhibitor (91 %). A quarter (26 %) experienced an ED visit and/or hospitalization during the observation period. Rates of ED visits and/or hospitalizations in the risk vs. control periods were similar, with an incidence rate ratio of 1.04 (95 % CI: 0.75-1.45). Subgroup and sensitivity analyses yielded similar results. CONCLUSION: Seasonal influenza vaccination was not associated with an increased incidence of ED visit or hospitalization among adults with cancer treated with ICI therapy and our results support further evidence of vaccine safety.

Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19.

BackgroundWaning immunity from seasonal influenza vaccination can cause suboptimal protection during peak influenza activity. However, vaccine effectiveness studies assessing waning immunity using vaccinated and unvaccinated individuals are subject to biases.AimWe examined the association between time since vaccination and laboratory-confirmed influenza to assess the change in influenza vaccine protection over time.MethodsUsing linked laboratory and health administrative databases in Ontario, Canada, we identified community-dwelling individuals aged ≥ 6 months who received an influenza vaccine before being tested for influenza by RT-PCR during the 2010/11 to 2018/19 influenza seasons. We estimated the adjusted odds ratio (aOR) for laboratory-confirmed influenza by time since vaccination (categorised into intervals) and for every 28 days.ResultsThere were 53,065 individuals who were vaccinated before testing for influenza, with 10,264 (19%) influenza-positive cases. The odds of influenza increased from 1.05 (95% CI: 0.91-1.22) at 42-69 days after vaccination and peaked at 1.27 (95% CI: 1.04-1.55) at 126-153 days when compared with the reference interval (14-41 days). This corresponded to 1.09-times increased odds of influenza every 28 days (aOR = 1.09; 95% CI: 1.04-1.15). Individuals aged 18-64 years showed the greatest decline in protection against influenza A(H1N1) (aORper 28 days = 1.26; 95% CI: 0.97-1.64), whereas for individuals aged ≥ 65 years, it was against influenza A(H3N2) (aORper 28 days = 1.20; 95% CI: 1.08-1.33). We did not observe evidence of waning vaccine protection for individuals aged < 18 years.ConclusionsInfluenza vaccine protection wanes during an influenza season. Understanding the optimal timing of vaccination could ensure robust protection during seasonal influenza activity.

Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.

Background: Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods: To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion: Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.