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(searched for: doi:10.1007/s11606-020-06130-4)
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Emily Hallgren, Ramey Moore, Rachel S. Purvis, Spencer Hall, , Sharon Reece, Sheena CarlLee, Morgan Gurel-Headley,
Human Vaccines & Immunotherapeutics pp 1-8; https://doi.org/10.1080/21645515.2021.2010427

Abstract:
To end the COVID-19 pandemic, it is essential to increase vaccine coverage in the United States (U.S.). In this study, we examine the facilitators that helped hesitant adopters – those who are both vaccinated and report some degree of hesitancy – overcome barriers to vaccination. Drawing on a sample of 867 hesitant adopters in Arkansas, we find social networks, individual actions, health care organizations and professionals, employers, religious communities and leaders, and the media all play a role in helping the vaccine hesitant overcome barriers to vaccination. Our findings demonstrate vaccine hesitancy and uptake occur simultaneously, and overcoming hesitancy in the U.S. population requires multifaceted strategies from multiple entities. We provide recommendations for overcoming barriers, including hesitancy, based on our findings.
Don E. Willis, , Keneshia Bryant‐Moore, James P. Selig, , Holly C. Felix, Geoffrey M. Curran,
Clinical and Translational Science, Volume 14, pp 2200-2207; https://doi.org/10.1111/cts.13077

Abstract:
Understanding and minimizing coronavirus disease 2019 (COVID-19) vaccine hesitancy is critical to population health and minimizing health inequities, which continue to be brought into stark relief by the pandemic. We investigate questions regarding vaccine hesitancy in a sample (n = 1205) of Arkansas adults surveyed online in July/August of 2020. We examine relationships among sociodemographics, COVID-19 health literacy, fear of COVID-19 infection, general trust in vaccines, and COVID-19 vaccine hesitancy using bivariate analysis and a full information maximum likelihood (FIML) logistic regression model. One in five people (21,21.86%) reported hesitancy to take a COVID-19 vaccine. Prevalence of COVID-19 vaccine hesitancy was highest among Black/African Americans (50.00%), respondents with household income less than $25K (30.68%), some college (32.17%), little to no fear of infection from COVID-19 (62.50%), and low trust in vaccines in general (55.84%). Odds of COVID-19 vaccine hesitancy were 2.42 greater for Black/African American respondents compared to White respondents (p< 0.001), 1.67 greater for respondents with some college/technical degree compared to respondents with a 4-year degree (p< 0.05), 5.48 greater for respondents with no fear of COVID-19 infection compared to those who fear infection to a great extent (p< 0.001), and 11.32 greater for respondents with low trust in vaccines (p< 0.001). Sociodemographic differences in COVID-19 vaccine hesitancy raise concerns about the potential of vaccine implementation to widen existing health disparities in COVID-19 related infections, particularly among Black/African Americans. Fear of infection and general mistrust in vaccines are significantly associated with vaccine hesitancy.
, Fedelis Mutiso, Noel T. Mueller, John L. Pearce, Sara E. Benjamin-Neelon
Published: 24 March 2021
Abstract:
Background: Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods: We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results: At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions: The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.
International Journal of Environmental Research and Public Health, Volume 18; https://doi.org/10.3390/ijerph18062826

Abstract:
Mobility restrictions have been a heated topic during the global pandemic of coronavirus disease 2019 (COVID-19). However, multiple recent findings have verified its importance in blocking virus spread. Evidence on the association between mobility, cases imported from abroad and local medical resource supplies is limited. To reveal the association, this study quantified the importance of inter- and intra-country mobility in containing virus spread and avoiding hospitalizations during early stages of COVID-19 outbreaks in India, Japan, and China. We calculated the time-varying reproductive number (R t) and duration from illness onset to diagnosis confirmation (D oc), to represent conditions of virus spread and hospital bed shortages, respectively. Results showed that inter-country mobility fluctuation could explain 80%, 35%, and 12% of the variance in imported cases and could prevent 20 million, 5 million, and 40 million imported cases in India, Japan and China, respectively. The critical time for screening and monitoring of imported cases is 2 weeks at minimum and 4 weeks at maximum, according to the time when the Pearson’s Rs between R t and imported cases reaches a peak (>0.8). We also found that if local transmission is initiated, a 1% increase in intra-country mobility would result in 1430 (±501), 109 (±181), and 10 (±1) additional bed shortages, as estimated using the D oc in India, Japan, and China, respectively. Our findings provide vital reference for governments to tailor their pre-vaccination policies regarding mobility, especially during future epidemic waves of COVID-19 or similar severe epidemic outbreaks.
, , Noel T Mueller, , Sara E Benjamin-Neelon
Published: 11 September 2020
Abstract:
Background: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed.Methods: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state.Results: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a “crossover effect” in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5.Conclusions: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.
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