COVID-19 Vaccination Among US-Born and Non–US-Born Residents of the United States From a Nationally Distributed Survey: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Extended literature has demonstrated that COVID-19 vaccination is crucial for the health of all individuals, regardless of age. Research on vaccination status in the United States (US) among US-born and non-US-born residents is limited. OBJECTIVE: The objective of our study was to examine COVID-19 vaccination during the pandemic among US-born and non-US-born people, while accounting for sociodemographic and socioeconomic factors gathered through a nationally distributed survey. METHODS: A descriptive analysis was conducted on a comprehensive 116-item survey distributed between May 2021 and January 2022 across the US by self-reported COVID-19 vaccination and US/non-US birth status. For participants that responded that they were not vaccinated, we asked if they were "not at all likely," "slightly to moderately likely," or "very to extremely likely" to be vaccinated. Race and ethnicity were categorized as White, Black or African American, Asian, American Indian or Alaskan Native, Hawaiian or Pacific Islander, African, Middle Eastern, and multiracial or multiethnic. Additional sociodemographic and socioeconomic variables included gender, sexual orientation, age group, annual household income, educational attainment, and employment status. RESULTS: The majority of the sample, regardless of whether they were US-born or non-US-born, reported being vaccinated (3639/5404, 67.34%). The US-born participants with the highest proportion of COVID-19 vaccination self-identified as White (1431/2753, 51.98%), while the highest proportion of vaccination among non-US-born participants was found among participants who self-identified as Hispanic/Latino (310/886, 34.99%). Comparing US-born and non-US-born participants showed that among those who were not vaccinated, the highest self-reported sociodemographic characteristics by proportion were similar between the groups, and included identifying as a woman, being straight or heterosexual, being aged 18 to 35 years, having an annual household income <$25,000, and being unemployed or taking part in nontraditional work. Among the 32.66% (1765/5404) of participants that reported not being vaccinated, 45.16% (797/1765) stated that they were not at all likely to seek vaccination. Examining US/non-US birth status and the likelihood to be vaccinated for COVID-19 among nonvaccinated participants revealed that the highest proportions of both US-born and non-US-born participants reported being not at all likely to seek vaccination. Non-US-born participants, however, were almost proportionally distributed in their likelihood to seek vaccination; they reported to be "very to extremely likely" to vaccinate (112/356, 31.46%); compared to 19.45% (274/1409) of US-born individuals reporting the same. CONCLUSIONS: Our study highlights the need to further explore factors that can increase the likelihood of seeking vaccination among underrepresented and hard-to-reach populations, with a particular focus on tailoring interventions for US-born individuals. For instance, non-US-born individuals were most likely to vaccinate when reporting COVID-19 nonvaccination than US-born individuals. These findings will aid in identifying points of intervention for vaccine hesitancy and promoting vaccine adoption during current and future pandemics.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».