Non-COVID-19 vaccine hesitancy among migrant populations worldwide: a scoping review of the literature, 2000-2020.

INTRODUCTION: Migration can be linked to the transmission of vaccine-preventable diseases. Hence, monitoring migrants' vaccination-related concerns can inform needed interventions to support vaccine acceptance. AREAS COVERED: Along with Google and Google Scholar, we searched 13 bibliographic databases between 1 January 2000 and 10 October 2020, to identify published studies of vaccine hesitancy among migrant populations. From a total of 8,915 records, we screened 745 abstracts and included 112 eligible articles. We summarized extracted data using figures, tables, and narrations. Of the 112 articles, 109 were original quantitative (48%), qualitative (45%), and mixed-methods (7%) research, originating mainly from the United States (US) (68%), the United Kingdom (UK) (12%), and Scandinavia (6%). Most articles addressed human papillomavirus (63%), measles (13%), and influenzas (9%) vaccinations, and the leading sponsor of funded research was the US National Institutes of Health (50%). Discernable migrant groups with vaccine-specific concerns included Somali diasporas, UK-based Poles and Romanians, and US-based Haitians and Koreans. Among US-based Latina/Latino immigrants, lower vaccine uptake frequency was mostly associated with awareness levels, knowledge gaps, and uninsured status. EXPERT OPINION: Migrants' vaccine-related apprehensions may cascade well beyond their proximate social connections and influence vaccine attitudes and behaviors in their countries-of-origin.

Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.

BACKGROUND: In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open." METHODS: We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain. RESULTS: We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding. CONCLUSION: This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.