Introduction: Diverse algorithms for signal detection exist. However, inconsistent results are often encountered among the algorithms due to different levels of specificity used in defining the adverse events (AEs) and signal threshold. We aimed to explore potential safety signals for two pneumococcal vaccines in a spontaneous reporting database and compare the results and performances among the algorithms. Methods: Safety surveillance was conducted using the Korea national spontaneous reporting database from 1988 to 2017. Safety signals for pneumococcal vaccine and its subtypes were detected using the following the algorithms: disproportionality methods comprising of proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC); empirical Bayes geometric mean (EBGM); and tree-based scan statistics (TSS). Moreover, the performances of these algorithms were measured by comparing detected signals with the known AEs or pneumococcal vaccines (reference standard). Results: Among 10,380 vaccine-related AEs, 1135 reports and 101 AE terms were reported following pneumococcal vaccine. IC generated the most safety signals for pneumococcal vaccine (40/101), followed by PRR and ROR (19/101 each), TSS (15/101), and EBGM (1/101). Similar results were observed for its subtypes. Cellulitis was the only AE detected by all algorithms for pneumococcal vaccine. TSS showed the best balance in the performance: the highest in accuracy, negative predictive value, and area under the curve (70.3%, 67.4%, and 64.2%). Conclusion: Discrepancy in the number of detected signals was observed between algorithms. EBGM and TSS calibrated noise better than disproportionality methods, and TSS showed balanced performance. Nonetheless, these results should be interpreted with caution due to a lack of a gold standard for signal detection.

OBJECTIVES: This study explored predictors of coronavirus disease 2019 (COVID-19) booster hesitancy among fully vaccinated young adults and parental COVID-19 vaccine hesitancy for their children.METHODS: This cross-sectional study administered an online survey from December 2 to December 20, 2021. We enrolled participants aged 18-49 years, for whom ≥2 weeks had passed after their initial COVID-19 vaccination. We estimated odds ratios (ORs) with 95% confidence intervals (CIs) using multivariate logistic regression to evaluate factors associated with booster/vaccine hesitancy.RESULTS: Among the 2,993 participants, 48.8% showed hesitancy (wait and see: 40.2%; definitely not: 8.7%). Booster hesitancy was more common among women (OR, 1.25; 95% CI, 1.05 to 1.50), younger people (OR, 1.44; 95% CI, 1.17 to 1.77), those with a lower education level (OR, 2.05; 95% CI, 1.10 to 3.82), those who received the mRNA-1273 vaccine type (OR, 2.01; 95% CI, 1.65 to 2.45), and those who experienced serious adverse events following previous COVID-19 vaccination (OR, 2.03; 95% CI, 1.47 to 2.80). The main reasons for booster hesitancy were concerns about safety (54.1%) and doubts about efficacy (29.8%). Among the 1,020 respondents with children aged <18 years, 65.8% were hesitant to vaccinate their children against COVID-19; hesitancy was associated with younger parental age, education level, the type of vaccine the parent received, and a history of COVID-19 infection.CONCLUSIONS: Concerns about the efficacy and safety of COVID-19 vaccines were the major barrier to booster acceptance. The initial COVID-19 vaccine type (mRNA-1273), young age, gender (women), a low education level, and adverse events after the first COVID-19 vaccine were key predictors of booster hesitancy.

Purpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.

OBJECTIVES: This study investigated the reporting rates of adverse events following immunization (AEFIs) to the spontaneous reporting system (SRS) and its predictors among individuals with AEFIs after coronavirus disease 2019 (COVID-19) vaccination.METHODS: A cross-sectional, web-based survey was conducted from December 2, 2021 to December 20, 2021, recruiting participants >14 days after completion of a primary COVID-19 vaccination series. Reporting rates were calculated by dividing the number of participants who reported AEFIs to the SRS by the total number of participants who experienced AEFIs. We estimated adjusted odds ratios (aORs) using multivariate logistic regression to determine factors associated with spontaneous AEFIs reporting.RESULTS: Among 2,993 participants, 90.9% and 88.7% experienced AEFIs after the first and second vaccine doses, respectively (reporting rates, 11.6 and 12.7%). Furthermore, 3.3% and 4.2% suffered moderate to severe AEFIs, respectively (reporting rates, 50.5 and 50.0%). Spontaneous reporting was more prevalent in female (aOR, 1.54; 95% confidence interval [CI], 1.31 to 1.81); those with moderate to severe AEFIs (aOR, 5.47; 95% CI, 4.45 to 6.73), comorbidities (aOR, 1.31; 95% CI, 1.09 to 1.57), a history of severe allergic reactions (aOR, 2.02; 95% CI, 1.47 to 2.77); and those who had received mRNA-1273 (aOR, 1.25; 95% CI, 1.05 to 1.49) or ChAdOx1 (aOR, 1.62; 95% CI, 1.15 to 2.30) vaccines versus BNT162b2. Reporting was less likely in older individuals (aOR, 0.98; 95% CI, 0.98 to 0.99 per 1-year age increment).CONCLUSIONS: Spontaneous reporting of AEFIs after COVID-19 vaccination was associated with younger age, female sex, moderate to severe AEFIs, comorbidities, history of allergic reactions, and vaccine type. AEFIs under-reporting should be considered when delivering information to the community and in public health decision-making.

The clinical importance of adhering to the regimen in tuberculosis patients has been widely investigated, but most studies were conducted in controlled settings and in limited populations. We aimed to measure the level of real-world adherence during intensive phase and investigate the predictors and the risk of mortality and health outcomes of intensive phase non-adherence in tuberculosis patients. We conducted a nationwide cohort study by linking the Korean National Tuberculosis Surveillance System and the National Health Information Database. We included all incident drug-susceptible tuberculosis patients who initiated the regimens recommended by the World Health Organization from 2013 to 2018. Adherence was measured using the proportion of days covered (poor [<50%], moderate [50%-79%], and high [≥80%]). We used logistic regression model to assess predictors and the Cox proportional hazard model to evaluate the risk of mortality and health outcomes with intensive phase non-adherence. Of 46,818 patients, there were 8% and 11% with poor and moderate adherent groups, respectively. Age ≥45 years, insulin use, and history of renal failure were predictors of non-adherence. Compared with high adherent group, poor and moderate adherent groups were associated with a substantial risk of mortality (poor: hazard ratio, 2.14 [95% confidence interval, 1.95-2.34]; moderate: 1.76 [1.62-1.92]). Similar trends were observed for health outcomes. Stratified analyses showed a higher risk of mortality in patients with medical aid, low income, and history of renal failure, systematic corticosteroids, and immunomodulators. Non-adherence during intensive phase increased mortality risk by twofold, underscoring targeted intervention for high-risk population, including advanced diabetes, and immunocompromised patients.