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. 2023 Sep 12:7:e41364.
doi: 10.2196/41364.

Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study

Li-Yin Lin #  1 , Chun-Ji Lin #  2 , Chen-I Kuan  3 , Hung-Yi Chiou  2   4
Affiliations

Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study

Li-Yin Lin et al. JMIR Form Res. .

Abstract

Background: Although vaccination has been shown to be one of the most important interventions, COVID-19 vaccine hesitancy remains one of the top 10 global public health challenges worldwide.

Objective: The objective of this study is to investigate (1) major determinants of vaccine hesitancy, (2) changes in the determinants of vaccine hesitancy at different time periods, and (3) the potential factors affecting vaccine acceptance.

Methods: This study applied a mixed methods approach to explore the potential determinants contributing to vaccine hesitancy among the Taiwanese population. The quantitative design of this study involved using Google Trends search query data. We chose the search term "" (vaccine), selected "" (Taiwan) as the location, and selected the period between December 18, 2020, and July 31, 2021. The rising keywords related to vaccine acceptance and hesitancy were collected. Based on the responses obtained from the qualitative study and the rising keywords obtained in Google Trends, the 3 most popular keywords related to vaccine hesitancy were identified and used as search queries in Google Trends between December 18, 2020, and July 31, 2021, to generate relative search volumes (RSVs). Lastly, autoregressive integrated moving average modeling was used to forecast the RSVs for the 3 keywords between May 29 and July 31, 2021. The estimated RSVs were compared to the observed RSVs in Google Trends within the same time frame.

Results: The 4 prevailing factors responsible for COVID-19 vaccine acceptance and hesitancy were doubts about the government and manufacturers, side effects, deaths associated with vaccination, and efficacy of vaccination. During the vaccine observation period, "political role" was the overarching consideration leading to vaccine hesitancy. During the peak of the pandemic, side effects, death, and vaccine protection were the main factors contributing to vaccine hesitancy. The popularity of the 3 frequently searched keywords "side effects," "vaccine associated deaths," and "vaccine protection" continued to rise throughout the pandemic outbreak. Lastly, the highest Google search queries related to COVID-19 vaccines emerged as "side effects" prior to vaccination, deaths associated with vaccines during the period when single vaccines were available, and "side effects" and "vaccine protection" during the period when multiple vaccines were available.

Conclusions: Investigating the key factors influencing COVID-19 vaccine hesitancy appears to be a fundamental task that needs to be undertaken to ensure effective implementation of COVID-19 vaccination. Google Trends may be used as a complementary infoveillance tool by government agencies for future vaccine policy implementation and communication.

Keywords: COVID-19; google trends; health promotion; infoveillance; public health; thematic analysis; vaccination; vaccine acceptance; vaccine hesitancy.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Relative search volumes of COVID-19 vaccine–related web search queries from December 18, 2020, to July 31, 2021, in Taiwan.
Figure 2
Figure 2
Keywords being searched on Google Trends: “vaccines and deaths.” Statistical analysis was performed using autoregressive integrated moving average modeling. The model was used to observe responses between December 18, 2020 (vaccine production), and May 28, 2021 (vaccination rollout in Taiwan). The time period for comparison was May 29 to July 31, 2021.
Figure 3
Figure 3
Keywords being searched on Google Trends: “vaccines and side effects.” Statistical analysis was performed using autoregressive integrated moving average modeling. The model was used to observe responses between December 18, 2020 (vaccine production), and May 28, 2021 (vaccination rollout in Taiwan). The time period for comparison was May 29 to July 31, 2021.
Figure 4
Figure 4
Keywords being searched on Google Trends: “vaccine and protection.” Statistical analysis was performed using autoregressive integrated moving average modeling. The model was used to observe responses between December 18, 2020 (vaccine production), and May 28, 2021 (vaccination rollout in Taiwan). The time period for comparison was May 29 to July 31, 2021.
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