Afzal Afsheen, Shariff Masood A, Perez-Gutierrez Victor, Khalid Amnah, Pili Christina, Pillai Anjana, Venugopal Usha, Kasubhai Moiz, Kanna Balavenkatesh, Poole Brian D, Pickett Brett E, Redd David S, Menon Vidya
Department of Medicine, NYC Health and Hospitals/Lincoln, Bronx, NY 10451, USA.
Research Administration, NYC Health and Hospitals/Central Office, New York, NY 10013, USA.
Vaccines (Basel). 2022 Feb 10;10(2):273. doi: 10.3390/vaccines10020273.
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.
尽管已经研发出几种有效的疫苗，但新冠病毒仍在继续传播，导致未接种疫苗的人群患上重病。医疗保健专业人员是有关疫苗接种的可靠信息来源，因此了解医疗保健专业人员对疫苗的态度和信念至关重要。在疫苗刚出现时，我们开展了一项基于调查的研究，以了解影响纽约市卫生与医院系统中医疗保健专业人员对新冠疫苗态度的因素，并收到了3759份回复。运用机器学习和卡方分析来确定最能预测疫苗犹豫的因素。研究发现，人口统计学因素、教育程度、在医院的角色、对疫情本身的看法以及工作和居住地点都对疫苗态度有显著影响。对居住地点从行政区和社区层面进行了考察，发现其对疫苗接受度有重大影响。有趣的是，这种行政区层面的数据与各行政区的病例数量或严重程度并不对应，这表明当地的社会或其他影响可能有很大作用。在准备支持疫苗接种的信息或活动时，应充分考虑当地和人口统计学因素。
