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Shen Z, Li Q, Wu J, Zhu D, Bai J, Ren R, Zhang J, Li Y, Wang M, Gu J, Li Y, Dong W, Wang H, Sun T, Yang F, Zhou X, Yang J, Tarimo CS, Ma M, Feng Y, Miao Y. Dynamic evolution of COVID-19 vaccine hesitancy over 2021-2023 among Chinese population: Repeated nationwide cross-sectional study. J Med Virol 2024; 96:e29800. [PMID: 39014958 DOI: 10.1002/jmv.29800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/29/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024]
Abstract
Globally, the rollout of COVID-19 vaccine had been faced with a significant barrier in the form of vaccine hesitancy. This study adopts a multi-stage perspective to explore the prevalence and determinants of COVID-19 vaccine hesitancy, focusing on their dynamic evolutionary features. Guided by the integrated framework of the 3Cs model (complacency, confidence, and convenience) and the EAH model (environmental, agent, and host), this study conducted three repeated national cross-sectional surveys. These surveys carried out from July 2021 to February 2023 across mainland China, targeted individuals aged 18 and older. They were strategically timed to coincide with three critical vaccination phases: universal coverage (stage 1), partial coverage (stage 2), and key population coverage (stage 3). From 2021 to 2023, the surveys examined sample sizes of 29 925, 6659, and 5407, respectively. The COVID-19 vaccine hesitation rates increased from 8.39% in 2021 to 29.72% in 2023. Urban residency, chronic condition, and low trust in vaccine developer contributed to significant COVID-19 vaccine hesitancy across the pandemic. Negative correlations between the intensity of vaccination policies and vaccine hesitancy, and positive correlations between vaccine hesitancy and long COVID, were confirmed. This study provides insights for designing future effective vaccination programs for emerging vaccine-preventable infectious X diseases.
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Affiliation(s)
- Zhanlei Shen
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Quanman Li
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Jian Wu
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Dongfang Zhu
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Junwen Bai
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Ruizhe Ren
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Jingbao Zhang
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Yi Li
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Meiyun Wang
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Jianqin Gu
- School of Medicine, Southern University of Science and Technology, Guangdong, China
| | - Yinfei Li
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Wenyong Dong
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Haipeng Wang
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Tao Sun
- Department of Health Policy and Management, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Fan Yang
- School of Public Health, Fudan University, NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Xue Zhou
- College of Health Management, Mudanjiang Medical University, Mudanjiang, Heilongjiang, China
| | - Jian Yang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Clifford Silver Tarimo
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Mingze Ma
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Yifei Feng
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
| | - Yudong Miao
- Department of Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- Henan Province Engineering Research Center of Health Economy & Health Technology Assessment, Zhengzhou, China
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Wu J, Shen Z, Li Q, Tarimo CS, Wang M, Gu J, Wei W, Zhang X, Huang Y, Ma M, Xu D, Ojangba T, Miao Y. How urban versus rural residency relates to COVID-19 vaccine hesitancy: A large-scale national Chinese study. Soc Sci Med 2023; 320:115695. [PMID: 36736053 PMCID: PMC9846885 DOI: 10.1016/j.socscimed.2023.115695] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 12/08/2022] [Accepted: 01/16/2023] [Indexed: 01/19/2023]
Abstract
RATIONALE Although urban-rural residency has been shown to influence individual COVID-19 vaccine hesitancy, the differences between urban and rural China have yet to be uncovered. OBJECTIVE This study aims to assess the current prevalence and factors associated with COVID-19 vaccine hesitancy in urban and rural areas and explore whether the rural versus urban residency is associated with COVID-19 vaccine hesitancy. METHODS A national, cross-sectional, online survey among Chinese urban and rural adults (≥18 years old) was conducted from 6th to August 9, 2021. A questionnaire was used to collect data on sociodemographic factors, perceptions of the COVID-19 pandemic and vaccination status. A multivariable logistic regression model was used to identify the factors that influence COVID-19 vaccine hesitancy. Propensity score matching (PSM) analysis was performed to explore the association between urban versus rural residency and COVID-19 vaccine hesitancy. RESULTS In total, 29,925 participants (80.56% urban participants) were recruited. Urban participants had a higher COVID-19 vaccine hesitancy than their rural counterparts (9.39% vs. 4.26%). After adjusting for potential confounders, we found that COVID-19 vaccine hesitancy among females was lower than that in males in both urban (aOR = 0.78, 95% CI [0.69-0.88]) and rural areas (aOR = 0.54, 95% CI [0.39-0.75]). The lack of trust towards vaccine producers was found to be associated with vaccine hesitancy among the urban participants (aOR = 2.76, 95% CI [2.22-3.43]). The rural floating population had a lower COVID-19 vaccine hesitancy than the rural permanent residents (aOR = 0.58, 95% CI [0.42-0.80]). PSM analysis revealed a 2.38% difference in COVID-19 vaccine hesitancy between urban and rural participants. CONCLUSIONS Urban participants were more hesitant to receive the COVID-19 vaccine than rural participants. Priority should be placed on boosting confidence in the healthcare system to reduce COVID-19 vaccine hesitancy among urban residents. Furthermore, we advocate for extra incentives and vaccination education for rural permanent residents.
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Affiliation(s)
- Jian Wu
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Zhanlei Shen
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Quanman Li
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Clifford Silver Tarimo
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China; Department of Science and Laboratory Technology, Dar es salaam Institute of Technology, Dar es Salaam, Tanzania
| | - Meiyun Wang
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Jianqin Gu
- School of Medicine, Southern University of Science and Technology, Guangdong, People's Republic of China
| | - Wei Wei
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Xinyu Zhang
- School of Public Health, Tianjin Medical University, Tianjin, People's Republic of China
| | - Yanli Huang
- Manage and service Center of Wuhou Medical Institutes, Sichuan, People's Republic of China
| | - Mingze Ma
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Dongyang Xu
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Theodora Ojangba
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China
| | - Yudong Miao
- Department of Health Management, College of Public Health, Zhengzhou University, Henan, People's Republic of China; Henan Province Engineering, Research Center of Health Economy & Health Technology Assessment, Zhengzhou, Henan, People's Republic of China.
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COVID-19 Vaccine-Associated Optic Neuropathy: A Systematic Review of 45 Patients. Vaccines (Basel) 2022; 10:vaccines10101758. [PMID: 36298623 PMCID: PMC9609672 DOI: 10.3390/vaccines10101758] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/15/2022] [Accepted: 10/19/2022] [Indexed: 11/20/2022] Open
Abstract
We provide a systematic review of published cases of optic neuropathy following COVID-19 vaccination. We used Ovid MEDLINE, PubMed, and Google Scholar. Search terms included: “COVID-19 vaccination”, “optic neuropathy”, “optic neuritis”, and “ischemic optic neuropathy”. The titles and abstracts were screened, then the full texts were reviewed. Sixty eyes from forty-five patients (28 females) were included. Eighteen eyes from fourteen patients (31.1%) were diagnosed with anterior ischemic optic neuropathy (AION), while 34 eyes from 26 patients (57.8%) were diagnosed with optic neuritis (ON). Other conditions included autoimmune optic neuropathy and Leber hereditary optic neuropathy. Fifteen patients (33.3%) had bilateral involvement. The mean age of all patients was 47.4 ± 17.1 years. The mean age of AION patients was 62.9 ± 12.2 years and of ON patients was 39.7 ± 12.8 years (p < 0.001). The mean time from vaccination to ophthalmic symptoms was 9.6 ± 8.7 days. The mean presenting visual acuity (VA) was logMAR 0.990 ± 0.924. For 41 eyes with available follow-up, the mean presenting VA was logMAR 0.842 ± 0.885, which improved to logMAR 0.523 ± 0.860 at final follow-up (p < 0.001). COVID-19 vaccination may be associated with different forms of optic neuropathy. Patients diagnosed with ON were more likely to be younger and to experience visual improvement. More studies are needed to further characterize optic neuropathies associated with COVID-19 vaccination.
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Abstract
Purpose : To report a case of anterior ischemic optic neuropathy (AION) following COVID-19 vaccination and provide a systematic review of all published cases of optic neuropathy following COVID-19 vaccination. Methods : A systematic literature search was performed using PubMed and Ovid MEDLINE for cases of optic neuropathy following COVID-19 vaccination. Terms used in the search included “COVID-19 vaccination”, “optic neuropathy”, “optic neuritis”, and “ischemic optic neuropathy”. Titles and abstracts were initially screened then full texts of eligible studies were reviewed for data extraction. Only cases published in the English language, peer reviewed, and that included details on optic nerve involvement were included. All study types were eligible for inclusion. Results : Including our patient, a total of 10 patients (8 females) were identified as developing optic neuropathy following COVID-19 vaccination. Five patients (50.0%) were diagnosed with AION, while 4 (40.0%) were diagnosed with optic neuritis. One patient was diagnosed with papillitis and neuroretinitis. Three patients (30.0%) had bilateral involvement. Mean age of patients was 48.5±19.7 years. Mean time from vaccination to onset of ophthalmic symptoms was 6.5±6.4 days. Median (IQR) presenting visual acuity was logMAR 0.3 (0-1). For the 8 eyes which had both presenting and final follow-up visual acuity, median (IQR) presenting vision was logMAR 0.2 (0-0.7) and at final follow-up was logMAR 0 (0-0.05) (P=0.184). Conclusion : COVID-19 vaccination may result in optic neuropathy in the form of optic neuritis and ischemic optic neuropathy. Further studies are needed to determine the incidence, management, and prognosis of optic neuropathies associated with COVID-19 vaccination.
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