Hou, Zhiyuan; Lai, Xiaozhen; Liu, Yang; Jit, Mark; Larson, Heidi J; Fang, Hai; (2023) Assessing the impact of the 2018 Changchun Changsheng vaccine incident on childhood vaccination in China. Communications medicine, 3 (1). 114-. ISSN 2730-664X DOI: https://doi.org/10.1038/s43856-023-00339-0
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Abstract
BACKGROUND: The 2018 Changchun Changsheng vaccine incident is an emergent public health event in China with reports of DTaP vaccines with compromised efficacy. This study aimed to estimate the impact of the vaccine incident on real-world vaccination behaviors in China. METHODS: A cross-sectional survey was conducted in ten provinces in 2019. Vaccination records were collected from 5294 children aged 6-59 months, with information on 75,579 vaccine doses for seven National Immunization Program (NIP) vaccines and two non-NIP vaccines received from 2014 to 2019. Chi-square test, interrupted time series, and logistic regression were used to evaluate the impacts of vaccine incident on vaccination delay, measured as the proportion of delayed doses out of total doses in schedule. RESULTS: Here we show significant increases in doses delayed ≤ 3 months (19.12% to 22.51%, p = 0.000) and > 3 months (7.17% to 11.82%, p = 0.000) for DTaP vaccine after the incident. By scaling nationally, there will be extra 2.1 million doses delayed ≤ 3 months and 2.8 million doses delayed > 3 months in the year following this incident. More guardians choose expensive private-market substitutes containing DTaP elements over government-funded DTaP vaccines. Controlling for socio-demographic factors, doses scheduled after the incident have higher odds of delays for DTaP vaccine (OR: 3.49, 95% CI: 3.08-3.96) and other NIP vaccines (OR: 2.76, 95% CI: 2.55-2.99), but not for non-NIP vaccines. CONCLUSIONS: The observed delays in the incident-involved DTaP vaccine immunization reflect the negative effects of the vaccine incident on vaccination behaviors under the NIP. However, its effects seem minimal for non-NIP vaccines.
Item Type | Article |
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Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology & Dynamics (2023-) |
Research Centre | Centre for the Mathematical Modelling of Infectious Diseases |
PubMed ID | 37608146 |
Elements ID | 208068 |
Official URL | http://dx.doi.org/10.1038/s43856-023-00339-0 |
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