An increasing number of myocarditis/pericarditis incidences has been reported after coronavirus disease-19 (COVID-19) vaccination in adolescents and young adults. This study was designed to investigate the incidence rate of—and risk for—myocarditis and pericarditis following COVID-19 vaccination in the United States according to age and vaccine type. This study used the Vaccine Adverse Events Reporting System (VAERS) from 11 December 2020 to 13 August 2021. A population-based data mining approach was performed based on the reporting odds ratio (ROR). Adverse events of myocarditis and pericarditis following COVID-19 vaccination were rare, with an incidence rate of 5.98 (95% CI = 5.73–6.24) cases per million doses administered. The incidence rate was higher in adolescents and after the administration of the second dose of messenger RNA (mRNA) vaccines. Overall, two mRNA vaccines were significantly associated with increased risks for myocarditis/pericarditis (mRNA-1273 (Moderna): ROR = 2.91, 95% CI = 2.21–3.83; BNT162b2 (Pfizer–BioNTech): ROR = 5.37, 95% CI = 4.10–7.04) compared to all other vaccines from VAERS. The viral vector vaccine of Ad26.COV2.S (Janssen) was not associated with signals of myocarditis/pericarditis (ROR = 1.39; 95% CI = 0.99–1.97). This study found increased risks for myocarditis/pericarditis following mRNA COVID-19 vaccines. For patients at high risk for myocarditis/pericarditis or with myocardial injuries, the viral vector vaccine may be an alternative for consideration.

Background: The emergency use authorization for coronavirus disease 2019 (COVID-19) vaccines brought both hopes and concerns to the Americans and others. We aimed to estimate the mortality rate of COVID-19 vaccination and presented characteristics of deaths following COVID-19 vaccination.Methods: Data on deaths following COVID-19 vaccination were obtained from the Vaccine Adverse Event Reporting System (VAERS) from December 11, 2020 through January 8, 2021. The Centers for Disease Control and Prevention (CDC) COVID Data Tracker was used to identify the total number of people receiving COVID-19 vaccines during the same period to estimate the mortality rate. Stratified analysis was conducted by the location of vaccination.Results: As of January 8, 2021, 55 deaths were reported, and the mortality rate of COVID-19 vaccination was 8.2 per million population. A total of 37 deaths were reported among long-term care facility residents, and the mortality rate was 53.4 per million population. Top reported comorbidities associated with deaths included hypertension, dementia, chronic obstructive pulmonary disease (COPD), diabetes, and heart failure. In addition, dementia was more likely to be associated with deaths vaccinated at long-term care facilities than at other locations.Conclusion: The benefits of COVID-19 vaccines outweigh the potential risks in older frail populations, and our findings do not support actions to exclude older adults from being vaccinated. However, continued monitoring of COVID-19 vaccination is still warranted.

Objective To study the impact of financial barriers to healthcare on health status, healthcare utilisation and costs among patients with cognitive impairment. Design Cross-sectional. Setting National Health Interview Survey (NHIS), 2011–2017. Participants Patients with cognitive impairment aged 18 years or older. Interventions Financial barriers to healthcare were identified using a series of NHIS prompts asking about the affordability of healthcare services. Primary outcome measures Health status was based on a survey prompt about respondents’ general health. Healthcare utilisation included office visits, home healthcare visits, hospital stays and emergency department (ED) visits. Economic burden was based on the family spending on medical care. Logistic regression models were used to examine the impact of financial barriers to healthcare access on health status, home healthcare visits, office visits, hospital stays and ED visits, respectively. Results Compared with cognitively impaired respondents without financial barriers to healthcare access, those with financial barriers were more likely to be unhealthy (OR 0.64, 95% CI 0.57 to 0.72). Cognitively impaired respondents with financial barriers were less likely to have home healthcare (OR 0.69, 95% CI 0.48 to 0.99) and more likely to have hospital stays (OR 1.33, 95% CI 1.19 to 1.48) and ED visits (OR 1.50, 95% CI 1.35 to 1.67). In addition, compared with cognitively impaired respondents without financial barriers to healthcare access, those with the barriers were more likely to have an increased economic burden (OR=1.85, 95% CI 1.65 to 2.07). Conclusion Financial barriers to healthcare worsened health status and increased use of ED, hospitalisation and economic burden. Policy decision-makers, providers and individuals with cognitive impairment should be aware of the impact of financial barriers and take corresponding actions to reduce the impact.

Background: Cancer survivors are vulnerable to have medication nonadherence. We aimed to estimate the impact of cost-related medication nonadherence on economic burdens, productivity loss, and functional abilities among cancer survivors.Methods: A cross-sectional study was conducted using data from the National Health Interview Survey (NHIS), 2011–2018. Cost-related medication nonadherence was identified based on NHIS prompts. An ordinal logistic regression model was used to determine the impact of cost-related medication nonadherence on survivors’ economic burden. Two negative binomial regression models were implemented to estimate the impact on productivity loss. In addition, four logistic regression models were used to determine the impact on functional abilities. The weighted analysis was used to generate national estimates.Results: Among 35, 773, 286 cancer survivors, 15, 002, 192 (41.9%) respondents reported that they experienced cost-related medication nonadherence. Compared to cancer survivors without cost-related medication nonadherence, those with nonadherence were significantly associated with an increased economic burden (OR: 1.89, 95% CI: 1.70–2.11). Also, cancer survivors with cost-related medication nonadherence were significantly more likely to have an increased bed disability day (IRR: 1.46, 95% CI: 1.21–1.76). In terms of the limitations, cancer survivors with nonadherence were significantly more likely to have both activity limitation (OR: 1.42, 95% CI: 1.25–1.60) and functional limitation (OR: 2.12, 95% CI: 1.81–2.49).Conclusion: Cost-related medication nonadherence increased economic burdens, productivity loss, and limitations in functional abilities among cancer survivors. Strategies are needed to help cancer survivors with cost-related medication nonadherence to be adherent to prescriptions.

The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups. This retrospective cohort study used a population-based dataset of COVID-19 cases from the Centers for Disease Control and Prevention (CDC), spanning the years 2020–2024. The study analyzed AI model performance across different racial and ethnic groups and employed transfer learning techniques to improve model fairness by adapting pre-trained models to the specific demographic and clinical characteristics of the population. Decision Tree (DT) and Random Forest (RF) models consistently showed improvements in accuracy, precision, and ROC-AUC scores for Non-Hispanic Black, Hispanic/Latino, and Asian populations. The most significant precision improvement was observed in the DT model for Hispanic/Latino individuals, which increased from 0.3805 to 0.5265. The precision for Asians or Pacific Islanders in the DT model increased from 0.4727 to 0.6071, and for Non-Hispanic Blacks, it rose from 0.5492 to 0.6657. Gradient Boosting Machines (GBM) produced mixed results, showing accuracy and precision improvements for Non-Hispanic Black and Asian groups, but declines for the Hispanic/Latino and American Indian groups, with the most significant decline in precision, which dropped from 0.4612 to 0.2406 in the American Indian group. Logistic Regression (LR) demonstrated minimal changes across all metrics and groups. For the Non-Hispanic American Indian group, most models showed limited benefits, with several performance metrics either remaining stable or declining. This study demonstrates the potential of AI in predicting COVID-19 mortality while also underscoring the critical need to address demographic biases. The application of transfer learning significantly improved the predictive performance of models across various racial and ethnic groups, suggesting these techniques are effective in mitigating biases and promoting fairness in AI models.