Introduction: New York City is one of the areas most affected by the COVID-19 pandemic in the United States. Healthcare workers are among those at high risk of contracting the virus, and a vital source of information and trust in vaccines to the community. Methods: This study was conducted about attitudes towards COVID-19 vaccination among healthcare workers at a public hospital in New York City during the beginning of COVID-19 vaccination. 428 hospital employees responded. Results: Several factors were significantly associated with vaccine attitudes, including demographics such as gender (p = 0.002), age (p = 0.005), race (p < 0.001) and home location (p < 0.001), role within the hospital (p < 0.001), knowledge about the virus (p < 0.001) and confidence in and expectations about personal protective equipment and behaviors (p < 0.001). Structural equation modeling revealed that the most predictive factors were prior vaccine attitudes and concern with the speed of testing and approval of the vaccines (p < 0.001). Multivariate analysis reinforced these, while also identifying perceived personal risk as significant (p = 0.033). Conclusions: Several modifiable factors that reflect confidence in science, scientific knowledge, personal risk perception, experience and medical authority are correlated with vaccine attitudes, indicating that a holistic educational approach to improve trust in science is likely to be effective in long-term reduction in vaccine hesitancy.

Despite the development of several effective vaccines, SARS-CoV-2 continues to spread, causing serious illness among the unvaccinated. Healthcare professionals are trusted sources of information about vaccination, and therefore understanding the attitudes and beliefs of healthcare professionals regarding the vaccines is of utmost importance. We conducted a survey-based study to understand the factors affecting COVID-19 vaccine attitudes among health care professionals in NYC Health and Hospitals, at a time when the vaccine was new, and received 3759 responses. Machine learning and chi-square analyses were applied to determine the factors most predictive of vaccine hesitancy. Demographic factors, education, role at the hospital, perceptions of the pandemic itself, and location of work and residence were all found to significantly contribute to vaccine attitudes. Location of residence was examined for both borough and neighborhood, and was found to have a significant impact on vaccine receptivity. Interestingly, this borough-level data did not correspond to the number or severity of cases in the respective boroughs, indicating that local social or other influences likely have a substantial impact. Local and demographic factors should be strongly considered when preparing pro-vaccine messages or campaigns.

Predicting the mortality risk of patients with Coronavirus Disease 2019 (COVID-19) can be valuable in allocating limited medical resources in the setting of outbreaks. This study assessed the role of a chest X-ray (CXR) scoring system in a multivariable model in predicting the mortality of COVID-19 patients by performing a single-center, retrospective, observational study including consecutive patients admitted with a confirmed diagnosis of COVID-19 and an initial CXR. The CXR severity score was calculated by three radiologists with 12 to 15 years of experience in thoracic imaging, based on the extent of lung involvement and density of lung opacities. Logistic regression analysis was used to identify independent predictive factors for mortality to create a predictive model. A validation dataset was used to calculate its predictive value as the AUROC. A total of 628 patients (58.1% male) were included in this study. Age (p < 0.001), sepsis (p < 0.001), S/F ratio (p < 0.001), need for mechanical ventilation (p < 0.001), and the CXR severity score (p = 0.005) were found to be independent predictive factors for mortality. We used these variables to develop a predictive model with an AUROC of 0.926 (0.891, 0.962), which was significantly higher than that of the WHO COVID severity classification, 0.853 (0.798, 0.909) (one-tailed p-value = 0.028), showing that our model can accurately predict mortality of hospitalized COVID-19 patients.