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Evaluating the accessibility and capacity of SARS-CoV-2 vaccination and analyzing convenience-related factors during the Omicron variant epidemic in Beijing, China.

Lu-Zhao FengMingyue JiangLuodan SuoMingyu XuXiaomei LiQing WangChengxu BaiJiang WuZheng XuWeizhong YangYuping DuanJuan Li
Published in: Human vaccines & immunotherapeutics (2023)
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination service system lacks standardized indicators to assess resource allocation. Moreover, data on specific vaccination-promoting measures is limited. This study aimed to evaluate vaccination accessibility and capacity and investigate convenience-related factors in China during the Omicron variant epidemic. We collected information on SARS-CoV-2 vaccination services among vaccination sites in Beijing. Analysis was performed using nearest neighbor, Ripley's K, hot spot analysis, and generalized estimating equations. Overall, 299 vaccination sites were included. The demand for the SARS-CoV-2 vaccine increased with the increase in daily new cases, and the number of staff administering vaccines should be increased in urban areas at the beginning of the epidemic. Providing vaccination for both children and adults, extending vaccination service hours, and offering a wider range of vaccine categories significantly increased the doses of vaccines administered (all P  < .05). The provision of mobile vaccination vehicles effectively increased the doses of vaccines administered to individuals aged ≥ 60 years ( P  < .05). The allocation of SARS-CoV-2 vaccination services should be adjusted according to geographic location, population size, and vaccination demands. Simultaneous provision of vaccination services for children and their guardians, flexible service hours, prompt innovative vaccine production, and tailored vaccination strategies can foster vaccination uptake.
Keyphrases
  • sars cov
  • respiratory syndrome coronavirus
  • healthcare
  • mental health
  • primary care
  • palliative care
  • young adults
  • machine learning
  • health information
  • big data