Occupational disparities in COVID-19 vaccine hesitancy in Japan

Takahashi, Shuko; Takahashi, Naomi; Sasaki, Satoshi; Nohara, Masaru; Kawachi, Ichiro

doi:10.1016/j.ssmph.2022.101226

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Across most models, these trends manifest within three overarching occupational groups: Manufacturing, Professional, scientific, and management, and administrative and waste management services, and Wholesale trade. Work on occupation by [ 164 ] have reported more hesitant populations among older workers (aged 40 to 59) in sectors such as service and manufacturing, along with those who are unemployed. Similarly, [ 165 ], in a national survey encompassing Japanese adults aged 20 and older, observed reduced likelihood of vaccination among scientists and researchers.…”

Section: Discussionmentioning

confidence: 99%

“…It’s important to note, however, that much of the existing work on vaccine hesitancy and occupation primarily focuses on roles within the medical sector, often classified as essential workers. Nevertheless, there remains limited research in this field [ 164 ]. On a related note, being employed was exclusively applicable to the probably not model.…”

Section: Discussionmentioning

confidence: 99%

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Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

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The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data’s potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…This study only included COVID-19 cases before any vaccine became widely available. Although they are not the correct data all over Japan in 2021, the overall prevalence of vaccine hesitancy was 5.5% in working-age adults in Japan [ 28 ]. As vaccines become even more ubiquitous, the efficacy of pneumonia as a predictor of COVID-19 will most likely change in future (however, it should be mentioned that the present study is not a current omicron stock).…”

Section: Discussionmentioning

confidence: 99%

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model

Fukui,

Inui,

Komatsu

et al. 2023

Cureus

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Background: In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis. Methods: This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student’s t -test, the chi-square test, and a CART analysis model. Results: Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristic of the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power. Conclusions: The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

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“…However, vaccine hesitancy, defined as a delay in accepting or refusing vaccines despite their availability (1), has emerged as a significant public health challenge, particularly magnified during the COVID-19 pandemic. Research on vaccine hesitancy encompasses various approaches, including epidemiological inquiries examining the predictive effects of structural factors and social demographics (2)(3)(4)(5)(6), alongside policy-oriented investigations into factors influencing vaccination willingness, such as safety, effectiveness, accessibility, and institutional trust (7)(8)(9)(10). Recent studies have also explored vaccine hesitancy through the lens of post-modern social theories, touching upon issues like neo-colonialism (11), political ideologies (12), racialism (13,14), conspiracy theories (15,16), and parental norms (17,18).…”

Section: Introductionmentioning

confidence: 99%

Heritage and hesitancy: how preference for traditional Chinese medicine influences vaccine attitudes

Lan,

Jin

2024

Front. Public Health

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IntroductionVaccine hesitancy, amplified by the COVID-19 pandemic, is a pressing public health challenge. This study investigates the association between Traditional Chinese Medicine (TCM) preference and COVID-19 vaccine hesitancy within China.MethodsThe study uses data from the 2021 Chinese General Social Survey (CGSS) (N = 2,690). Logistic regressions and Karlson-Holm-Breen (KHB) method are employed to analyzed the relationship between TCM preference and vaccine hesitancy.ResultsThe study reaffirms prior findings by revealing a robust and stable association between TCM preference and vaccine hesitancy, which remains unaffected by socioeconomic and demographic confounders, as well as institutional trust dynamics of healthcare system. Contrary to expectations, TCM enthusiasts do not exhibit vaccine hesitancy based on divergent epistemological views concerning vaccine risks and immunity acquisition compared to biomedicine.DiscussionThis research enriches understandings of the intricate relations between healthcare paradigms and vaccine attitudes, inviting further inquiry into the role of CAM in shaping vaccination behaviors across different cultures and contexts. The insights bear significant public health implications for enhancing vaccine acceptance and coverage, particularly among populations where CAM practices wield substantial influence.

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Occupational disparities in COVID-19 vaccine hesitancy in Japan

Cited by 4 publications

References 26 publications

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model

Heritage and hesitancy: how preference for traditional Chinese medicine influences vaccine attitudes

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